diff --git a/opt/hyper_heuristic.py b/opt/hyper_heuristic.py
new file mode 100644
index 0000000..5db4237
--- /dev/null
+++ b/opt/hyper_heuristic.py
@@ -0,0 +1,539 @@
+from opt.predictor import NeuralPredictor
+from opt.utils import *
+from core.interface import *
+from core.common import *
+
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
+
+
+class Heuristic:
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        return -1
+
+
+class LeastPoints(Heuristic):
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        machine_index, machine_points = [], []
+        for index in config.keys():
+            if len(cp_assign[index]) == 0:
+                return index
+            machine_index.append(index)
+            machine_points.append(sum([cp_points[cp_idx] for cp_idx in cp_assign[index]]))
+
+        return machine_index[np.argmin(machine_points)]
+
+
+class LeastNzTypes(Heuristic):
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        machine_index, machine_nozzle = [], []
+        for index in config.keys():
+            if len(cp_assign[index]) == 0:
+                return index
+            machine_index.append(index)
+            machine_nozzle.append([cp_nozzle[cp_idx] for cp_idx in cp_assign[index]])
+        index = np.argmin(
+            [len(set(nozzle)) + 1e-5 * sum(cp_points[c] for c in cp_assign[machine_idx]) for machine_idx, nozzle in
+             enumerate(machine_nozzle)])
+
+        return machine_index[index]
+
+
+class LeastCpTypes(Heuristic):
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        machine_index, machine_types = [], []
+        for index in config.keys():
+            machine_index.append(index)
+            machine_types.append(len(cp_assign[index]) + 1e-5 * sum(cp_points[cp] for cp in cp_assign[index]))
+
+        return machine_index[np.argmin(machine_types)]
+
+
+class LeastCpNzRatio(Heuristic):
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        machine_index, machine_nz_type, machine_cp_type = [], [], []
+        for index in config.keys():
+            if len(cp_assign[index]) == 0:
+                return index
+            machine_index.append(index)
+            machine_nz_type.append(set(cp_nozzle[cp_idx] for cp_idx in cp_assign[index]))
+            machine_cp_type.append(len(cp_assign[index]))
+
+        min_idx = np.argmin([(machine_cp_type[idx] + 1e-5 * sum(
+            cp_points[c] for c in cp_assign[machine_index[idx]])) / (len(machine_nz_type[idx]) + 1e-5) for idx in
+                             range(len(machine_index))])
+
+        return machine_index[min_idx]
+
+
+def nozzle_assignment(cp_points, cp_nozzle, cp_assign, head_num):
+    nozzle_points = defaultdict(int)
+
+    for cp_idx in cp_assign:
+        nozzle_points[cp_nozzle[cp_idx]] += cp_points[cp_idx]
+
+    while len(nozzle_points.keys()) > head_num:
+        del nozzle_points[min(nozzle_points.items(), key=lambda x: x[1])[0]]
+
+    sum_points = sum(nozzle_points.values())
+    nozzle_points = defaultdict(int, {k: v for k, v in nozzle_points.items() if v / sum_points >= 0.8 / head_num})
+    nozzle_heads = defaultdict(int, {k: 1 for k in nozzle_points.keys()})
+
+    while sum(nozzle_heads.values()) != head_num:
+        max_cycle_nozzle = None
+        for nozzle, head_cnt in nozzle_heads.items():
+            if max_cycle_nozzle is None or nozzle_points[nozzle] / head_cnt > nozzle_points[max_cycle_nozzle] / \
+                    nozzle_heads[max_cycle_nozzle]:
+                max_cycle_nozzle = nozzle
+
+        assert max_cycle_nozzle is not None
+        nozzle_heads[max_cycle_nozzle] += 1
+    return nozzle_heads, nozzle_points
+
+
+class LeastCycle(Heuristic):
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        machine_index, machine_cycle = [], []
+        for index, head_num in config.items():
+            assign_component = cp_assign[index]
+            if len(assign_component) == 0:
+                return index
+
+            nozzle_heads, nozzle_points = nozzle_assignment(cp_points, cp_nozzle, assign_component, head_num)
+            machine_index.append(index)
+            machine_cycle.append(
+                max(nozzle_points[nozzle] / head for nozzle, head in nozzle_heads.items()) + 1e-5 * sum(
+                    cp_points[c] for c in cp_assign[index]))
+
+        return machine_index[np.argmin(machine_cycle)]
+
+
+class LeastNzChange(Heuristic):
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        machine_index, machine_nozzle_change = [], []
+        for index, head_num in config.items():
+            assign_component = cp_assign[index]
+            if len(assign_component) == 0:
+                return index
+
+            heads_points = []
+            nozzle_heads, nozzle_points = nozzle_assignment(cp_points, cp_nozzle, assign_component, head_num)
+            for nozzle, head in nozzle_heads.items():
+                for _ in range(head):
+                    heads_points.append(nozzle_points[nozzle] / nozzle_heads[nozzle])
+            machine_index.append(index)
+            machine_nozzle_change.append(np.std(heads_points) + 1e-5 * sum(cp_points[c] for c in cp_assign[index]))
+
+        return machine_index[np.argmin(machine_nozzle_change)]
+
+
+class LeastPickup(Heuristic):
+    @staticmethod
+    def apply(cp_points, cp_nozzle, cp_assign, config: defaultdict[int]):
+        machine_index, machine_pick_up = [], []
+        for index, head_num in config.items():
+            assign_component = cp_assign[index]
+            if len(assign_component) == 0:
+                return index
+            nozzle_heads, nozzle_points = nozzle_assignment(cp_points, cp_nozzle, assign_component, head_num)
+
+            nozzle_level, nozzle_counter = defaultdict(int), defaultdict(int)
+            level_points = defaultdict(int)
+
+            for cp_idx in sorted(assign_component, key=lambda x: cp_points[x], reverse=True):
+                nozzle, points = cp_nozzle[cp_idx], cp_points[cp_idx]
+                if nozzle not in nozzle_heads.keys():
+                    continue
+                if nozzle_counter[nozzle] and nozzle_counter[nozzle] % nozzle_heads[nozzle] == 0:
+                    nozzle_level[nozzle] += 1
+                level = nozzle_level[nozzle]
+                level_points[level] = max(level_points[level], points)
+                nozzle_counter[nozzle] += 1
+            machine_index.append(index)
+            machine_pick_up.append(sum(points for points in level_points.values()) + 1e-5 * sum(
+                cp_points[idx] for idx in cp_assign[index]))
+
+        return machine_index[np.argmin(machine_pick_up)]
+
+
+class HyperHeuristicOpt(BaseOpt):
+    def __init__(self, machine_num, part_data, step_data, feeder_data=None):
+        super().__init__(None, part_data, step_data, feeder_data)
+        self.line_config = [MachineConfig() for _ in range(machine_num)]
+        # self.base_opt = FeederPriorityOpt
+        self.base_opt = CellDivisionOpt
+        self.heuristic_map = {
+            'p': LeastPoints,
+            'n': LeastNzTypes,
+            'c': LeastCpTypes,
+            'r': LeastCpNzRatio,
+            'k': LeastCycle,
+            'g': LeastNzChange,
+            'u': LeastPickup,
+            }
+        self.machine_num = machine_num
+        self.predictor = NeuralPredictor()
+
+        self.cp_feeders = defaultdict(int)
+        self.cp_nozzle = defaultdict(str)
+        self.cp_points = defaultdict(int)
+        self.cp_index = defaultdict(int)
+
+        part_points = defaultdict(int)
+        for _, data in self.step_data.iterrows():
+            part_points[data.part] += 1
+
+        division_part = []
+        for _, data in self.part_data.iterrows():
+            division_part.extend([part_points[data.part] / data.fdn for _ in range(data.fdn)])
+        division_points = sum(division_part) / len(division_part)
+
+        idx = 0
+        for cp_idx, data in self.part_data.iterrows():
+            self.cp_feeders[cp_idx] = 1
+
+            division_data = copy.deepcopy(data)
+            division_data['points'] = part_points[data.part]
+            feeder_limit, total_points = division_data.fdn, division_data.points
+            if feeder_limit != 1:
+                feeder_limit = round(min(max(total_points // division_points * 1.5, feeder_limit), total_points))
+                # feeder_limit = total_points         # 小规模数据启用
+            surplus_points = total_points % feeder_limit
+            for _ in range(feeder_limit):
+                division_data.fdn, division_data.points = 1, math.floor(total_points / feeder_limit)
+                if surplus_points:
+                    division_data.points += 1
+                    surplus_points -= 1
+
+                self.cp_points[idx], self.cp_nozzle[idx] = division_data.points, division_data.nz
+                self.cp_index[idx] = cp_idx
+                idx += 1
+
+        self.board_width = self.step_data['x'].max() - self.step_data['x'].min()
+        self.board_height = self.step_data['y'].max() - self.step_data['y'].min()
+
+    def generate_pattern(self):
+        """
+        Generates a random pattern.
+        :return: The generated pattern string.
+        """
+        return "".join([random.choice(list(self.heuristic_map.keys()))
+                        for _ in range(random.randrange(1, len(self.cp_points)))])
+
+    def convertor(self, component_list, individual):
+        component_num = len(self.cp_feeders.keys())
+
+        cp_assign = [[] for _ in range(self.machine_num)]
+        component_machine_assign = [[0 for _ in range(self.machine_num)] for _ in range(component_num)]
+        machine_assign_counter = [0 for _ in range(self.machine_num)]
+
+        for idx, div_cp_idx in enumerate(component_list):
+            h = individual[idx % len(individual)]
+            cp_idx = self.cp_index[div_cp_idx]
+            if self.cp_points[cp_idx] == 0:
+                continue
+            machine_config = defaultdict(int)  # 可被分配的机器索引-贴片头数
+            if sum(component_machine_assign[cp_idx][:]) < self.cp_feeders[cp_idx]:
+                for machine_index in range(self.machine_num):
+                    if component_machine_assign[cp_idx][machine_index] or machine_assign_counter[machine_index] < \
+                            self.predictor.max_placement_points:
+                        machine_config[machine_index] = self.line_config[machine_index].head_num
+                machine_index = self.heuristic_map[h].apply(self.cp_points, self.cp_nozzle, cp_assign, machine_config)
+            else:
+                for machine_index in range(self.machine_num):
+                    if component_machine_assign[cp_idx][machine_index]:
+                        machine_config[machine_index] = self.line_config[machine_index].head_num
+                machine_index = self.heuristic_map[h].apply(self.cp_points, self.cp_nozzle, cp_assign, machine_config)
+
+            cp_assign[machine_index].append(div_cp_idx)
+
+            if component_machine_assign[cp_idx][machine_index] == 0:
+                machine_assign_counter[machine_index] += 1
+                component_machine_assign[cp_idx][machine_index] = 1
+
+        return cp_assign
+
+    def crossover(self, parent1, parent2):
+        """
+        Attempt to perform crossover between two chromosomes.
+        :param parent1: The first parent.
+        :param parent2: The second parent.
+        :return: The two individuals after crossover has been performed.
+        """
+        point1, point2 = random.randrange(len(parent1)), random.randrange(len(parent2))
+        substr1, substr2 = parent1[point1:], parent2[point2:]
+        offspring1, offspring2 = "".join((parent1[:point1], substr2)), "".join((parent2[:point2], substr1))
+        return offspring1[:len(self.cp_points)], offspring2[:len(self.cp_points)]
+
+    def mutation(self, individual):
+        """
+        Attempts to mutate the individual by replacing a random heuristic in the chromosome by a generated pattern.
+        :param individual: The individual to mutate.
+        :return: The mutated individual.
+        """
+        pattern = list(individual)
+        mutation_point = random.randrange(len(pattern))
+        pattern[mutation_point] = self.generate_pattern()
+        return ''.join(pattern)[:len(self.cp_points)]
+
+    def initialize(self, population_size):
+        return [self.generate_pattern() for _ in range(population_size)]
+
+    def cal_ind_val(self, component_list, individual):
+        machine_cp_assign = self.convertor(component_list, individual)
+        component_number = len(self.cp_feeders)
+
+        machine_cp_points = [[0 for _ in range(component_number)] for _ in range(self.machine_num)]
+        for machine_idx in range(self.machine_num):
+            for idx in machine_cp_assign[machine_idx]:
+                machine_cp_points[machine_idx][self.cp_index[idx]] += self.cp_points[idx]
+        machine_cp_feeders = [[0 for _ in range(component_number)] for _ in range(self.machine_num)]
+
+        for cp_idx in range(component_number):
+            if self.cp_points[cp_idx] == 0:
+                continue
+            feeder_nums = self.cp_feeders[cp_idx]
+            for machine_idx in range(self.machine_num):
+                if machine_cp_points[machine_idx][cp_idx]:
+                    machine_cp_feeders[machine_idx][cp_idx] = 1
+                    feeder_nums -= 1
+            while feeder_nums > 0:
+                assign_machine = None
+                for machine_idx in range(self.machine_num):
+                    if machine_cp_points[machine_idx][cp_idx] == 0:
+                        continue
+                    if assign_machine is None:
+                        assign_machine = machine_idx
+                        continue
+                    if machine_cp_points[assign_machine][cp_idx] / machine_cp_feeders[assign_machine][cp_idx] \
+                            < machine_cp_points[machine_idx][cp_idx] / machine_cp_feeders[machine_idx][cp_idx]:
+                        assign_machine = machine_idx
+                machine_cp_feeders[assign_machine][cp_idx] += 1
+                feeder_nums -= 1
+        nozzle_type = defaultdict(str)
+        for idx, cp_idx in self.cp_index.items():
+            nozzle_type[cp_idx] = self.cp_nozzle[idx]
+
+        obj = []
+        for machine_idx in range(self.machine_num):
+            div_cp_points, div_cp_nozzle = defaultdict(int), defaultdict(str)
+            idx = 0
+            for cp_idx in range(component_number):
+                total_points = machine_cp_points[machine_idx][cp_idx]
+                if total_points == 0:
+                    continue
+                div_index = 0
+                div_points = [total_points // machine_cp_feeders[machine_idx][cp_idx] for _ in
+                              range(machine_cp_feeders[machine_idx][cp_idx])]
+                while sum(div_points) < total_points:
+                    div_points[div_index] += 1
+                    div_index += 1
+
+                for points in div_points:
+                    div_cp_points[idx] = points
+                    div_cp_nozzle[idx] = nozzle_type[cp_idx]
+                    idx += 1
+            obj.append(self.predictor.eval(div_cp_points, div_cp_nozzle,
+                                           self.board_width, self.board_height, self.line_config[machine_idx]))
+
+        return obj
+
+    def evaluate(self, assignment):
+        partial_step_data, partial_part_data = defaultdict(pd.DataFrame), defaultdict(pd.DataFrame)
+        for machine_index in range(self.machine_num):
+            partial_step_data[machine_index] = pd.DataFrame(columns=self.step_data.columns)
+            partial_part_data[machine_index] = self.part_data.copy(deep=True)
+            partial_part_data[machine_index]['points'] = 0
+
+        #  averagely assign available feeder
+        for part_index, data in self.part_data.iterrows():
+            feeder_limit = data.fdn
+            feeder_points = [assignment[machine_index][part_index] for machine_index in range(self.machine_num)]
+            if sum(feeder_points) == 0:
+                continue
+
+            for machine_index in range(self.machine_num):
+                partial_part_data[machine_index].loc[part_index, 'points'] = 0
+
+            for machine_index in range(self.machine_num):
+                if feeder_points[machine_index] == 0:
+                    continue
+
+                partial_part_data[machine_index].loc[part_index, 'fdn'] = 1
+                feeder_limit -= 1
+
+            while feeder_limit:
+                assign_machine = None
+                for machine_index in range(self.machine_num):
+                    if feeder_limit <= 0:
+                        break
+
+                    if feeder_points[machine_index] == 0:
+                        continue
+
+                    if assign_machine is None or feeder_points[machine_index] / \
+                            partial_part_data[machine_index].loc[part_index].fdn > feeder_points[
+                        assign_machine] / partial_part_data[assign_machine].loc[part_index].fdn:
+                        assign_machine = machine_index
+
+                assert assign_machine is not None
+                partial_part_data[assign_machine].loc[part_index, 'fdn'] += 1
+
+                feeder_limit -= 1
+
+            for machine_index in range(self.machine_num):
+                if feeder_points[machine_index] > 0:
+                    assert partial_part_data[machine_index].loc[part_index].fdn > 0  # assignment[machine_index][part_index]
+
+        # === assign placements ===
+        part2idx = defaultdict(int)
+        for idx, data in self.part_data.iterrows():
+            part2idx[data.part] = idx
+
+        machine_average_pos = [[0, 0] for _ in range(self.machine_num)]
+        machine_step_counter = [0 for _ in range(self.machine_num)]
+        part_step_data = defaultdict(list)
+        for _, data in self.step_data.iterrows():
+            part_step_data[part2idx[data.part]].append(data)
+
+        multiple_component_index = []
+        for part_index in range(len(self.part_data)):
+            machine_assign_set = []
+            for machine_index in range(self.machine_num):
+                if assignment[machine_index][part_index]:
+                    machine_assign_set.append(machine_index)
+
+            if len(machine_assign_set) == 1:
+                for data in part_step_data[part_index]:
+                    machine_index = machine_assign_set[0]
+
+                    machine_average_pos[machine_index][0] += data.x
+                    machine_average_pos[machine_index][1] += data.y
+
+                    machine_step_counter[machine_index] += 1
+
+                    partial_part_data[machine_index].loc[part_index, 'points'] += 1
+                    partial_step_data[machine_index] = pd.concat(
+                        [partial_step_data[machine_index], pd.DataFrame(data).T])
+
+            elif len(machine_assign_set) > 1:
+                multiple_component_index.append(part_index)
+
+        for machine_index in range(self.machine_num):
+            if machine_step_counter[machine_index] == 0:
+                continue
+            machine_average_pos[machine_index][0] /= machine_step_counter[machine_index]
+            machine_average_pos[machine_index][1] /= machine_step_counter[machine_index]
+
+        for part_index in multiple_component_index:
+            for data in part_step_data[part_index]:
+                idx = -1
+                min_dist = None
+                for machine_index in range(self.machine_num):
+                    if partial_part_data[machine_index].loc[part_index, 'points'] >= assignment[machine_index][part_index]:
+                        continue
+                    dist = (data.x - machine_average_pos[machine_index][0]) ** 2 + (
+                            data.y - machine_average_pos[machine_index][1]) ** 2
+                    if min_dist is None or dist < min_dist:
+                        min_dist, idx = dist, machine_index
+
+                assert idx >= 0
+                machine_step_counter[idx] += 1
+                machine_average_pos[idx][0] += (1 - 1 / machine_step_counter[idx]) * machine_average_pos[idx][0] \
+                                               + data.x / machine_step_counter[idx]
+                machine_average_pos[idx][1] += (1 - 1 / machine_step_counter[idx]) * machine_average_pos[idx][1] \
+                                               + data.y / machine_step_counter[idx]
+
+                partial_part_data[idx].loc[part_index, 'points'] += 1
+                partial_step_data[idx] = pd.concat([partial_step_data[idx], pd.DataFrame(data).T])
+
+        obj, result = [], []
+        for machine_index in range(self.machine_num):
+            rows = partial_part_data[machine_index]['points'] != 0
+            partial_part_data[machine_index] = partial_part_data[machine_index][rows]
+
+            opt = self.base_opt(self.line_config[machine_index], partial_part_data[machine_index],
+                                partial_step_data[machine_index])
+            opt.optimize(hinter=False)
+            info = evaluation(self.line_config[machine_index], partial_part_data[machine_index],
+                              partial_step_data[machine_index], opt.result)
+            obj.append(info.total_time)
+            result.append(opt.result)
+
+        return max(obj), result
+
+    def optimize(self):
+        # genetic-based hyper-heuristic
+        crossover_rate, mutation_rate = 0.6, 0.1
+        population_size, total_generation = 20, 50
+        group_size = 10
+
+        best_val = np.inf
+
+        component_list = list(range(len(self.cp_points)))
+        with tqdm(total=total_generation * group_size) as pbar:
+            pbar.set_description('hyper-heuristic algorithm process for PCB assembly line balance')
+            for _ in range(group_size):
+                random.shuffle(component_list)
+                new_population = []
+                population = self.initialize(population_size)
+
+                # calculate fitness value
+                pop_val = [max(self.cal_ind_val(component_list, individual)) for individual in population]
+
+                for _ in range(total_generation):
+                    population += new_population
+                    for individual in new_population:
+                        pop_val.append(max(self.cal_ind_val(component_list, individual)))
+
+                    select_index = GenOpe.get_top_kth(pop_val, population_size, reverse=False)
+                    population = [population[idx] for idx in select_index]
+                    pop_val = [pop_val[idx] for idx in select_index]
+
+                    # min-max convert
+                    max_val = max(pop_val)
+                    sel_pop_val = list(map(lambda v: max_val - v, pop_val))
+                    sum_pop_val = sum(sel_pop_val) + 1e-10
+                    sel_pop_val = [v / sum_pop_val + 1e-3 for v in sel_pop_val]
+
+                    # crossover and mutation
+                    new_population = []
+                    for pop in range(population_size):
+                        if pop % 2 == 0 and np.random.random() < crossover_rate:
+                            index1 = GenOpe.roulette_wheel_selection(sel_pop_val)
+                            while True:
+                                index2 = GenOpe.roulette_wheel_selection(sel_pop_val)
+                                if index1 != index2:
+                                    break
+
+                            offspring1, offspring2 = self.crossover(population[index1], population[index2])
+
+                            if np.random.random() < mutation_rate:
+                                offspring1 = self.mutation(offspring1)
+
+                            if np.random.random() < mutation_rate:
+                                offspring2 = self.mutation(offspring2)
+
+                            new_population.append(offspring1)
+                            new_population.append(offspring2)
+
+                    pbar.update(1)
+
+                machine_assign = self.convertor(component_list, population[0])
+
+                assignment_result = [[0 for _ in range(len(self.part_data))] for _ in range(self.machine_num)]
+                for machine_idx in range(self.machine_num):
+                    for idx in machine_assign[machine_idx]:
+                        assignment_result[machine_idx][self.cp_index[idx]] += self.cp_points[idx]
+
+                val, res = self.evaluate(assignment_result)
+                if best_val is None or val < best_val:
+                    best_val = val
+                    self.result = res
+
diff --git a/opt/param.pth b/opt/param.pth
new file mode 100644
index 0000000..fe0c812
Binary files /dev/null and b/opt/param.pth differ
diff --git a/opt/predictor.py b/opt/predictor.py
new file mode 100644
index 0000000..96bfa6b
--- /dev/null
+++ b/opt/predictor.py
@@ -0,0 +1,314 @@
+from core.common import *
+from opt.smm.basis import BaseOpt
+
+import torch
+
+
+class Predictor:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def training(self, params):
+        pass
+
+    @staticmethod
+    def testing(self, params):
+        pass
+
+    @staticmethod
+    def predict(self, cp_points, cp_nozzle, board_width=None, board_height=None):
+        pass
+
+
+class Net(torch.nn.Module):
+    def __init__(self, input_size, hidden_size=1000, output_size=1):
+        super(Net, self).__init__()
+        self.fc1 = torch.nn.Linear(input_size, hidden_size)
+        self.relu = torch.nn.ReLU()  # 激活函数
+        self.fc2 = torch.nn.Linear(hidden_size, hidden_size)
+        # self.relu1 = torch.nn.ReLU()  # 激活函数
+        self.fc3 = torch.nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        # x = self.relu(x)
+        x = self.fc2(x)
+        x = self.relu(x)
+        x = self.fc3(x)
+        return x
+
+
+class NeuralPredictor(Predictor, BaseOpt):
+    def __init__(self):
+        super().__init__()
+        self.min_placement_points = 10
+        self.max_placement_points = 1000
+
+        self.max_component_types = 30
+        self.default_feeder_limit = 1
+        self.max_nozzle_types = 4
+
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        self.net = Net(input_size=self.get_feature(), output_size=1).to(self.device)
+        self.net_file = 'opt/param.pth'
+        try:
+            self.net.load_state_dict(torch.load(self.net_file, map_location=self.device))
+        except:
+            warnings.warn('the parameters of neural net model load failed', UserWarning)
+
+    def init_weights(self):
+        for m in self.net.modules():
+            if isinstance(m, torch.nn.Linear):
+                torch.nn.init.xavier_uniform_(m.weight)
+                torch.nn.init.zeros_(m.bias)
+
+    def subobjective(self, cp_points, cp_nozzle, config):
+        if len(cp_points.keys()) or sum(cp_points.values()) == 0:
+            return 0, 0, 0, 0
+        nozzle_heads, nozzle_points = defaultdict(int), defaultdict(int)
+        for idx, points in cp_points.items():
+            if points == 0:
+                continue
+            nozzle = cp_nozzle[idx]
+            nozzle_points[nozzle] += points
+            nozzle_heads[nozzle] = 1
+
+        anc_round_counter = 0
+        while sum(nozzle_heads.values()) != config.head_num:
+            max_cycle_nozzle = None
+
+            for nozzle, head_num in nozzle_heads.items():
+                if max_cycle_nozzle is None or nozzle_points[nozzle] / head_num > nozzle_points[max_cycle_nozzle] / \
+                        nozzle_heads[max_cycle_nozzle]:
+                    max_cycle_nozzle = nozzle
+            assert max_cycle_nozzle is not None
+            nozzle_heads[max_cycle_nozzle] += 1
+
+        head_nozzle_assignment, min_cost = None, None
+
+        # generate initial nozzle group
+        nozzle_group = []
+        # averagely assign for the same type of nozzles, and generate nozzle group
+        nozzle_points_cpy = copy.deepcopy(nozzle_points)
+        for nozzle, heads in nozzle_heads.items():
+            points = nozzle_points_cpy[nozzle] // heads
+            for _ in range(heads):
+                nozzle_group.append([nozzle, points])
+            nozzle_points_cpy[nozzle] -= heads * points
+
+        for idx, [nozzle, _] in enumerate(nozzle_group):
+            if nozzle_points_cpy[nozzle]:
+                nozzle_group[idx][1] += 1
+                nozzle_points_cpy[nozzle] -= 1
+
+        while True:
+            # assign nozzle group to each head
+            nozzle_group.sort(key=lambda x: -x[1])
+
+            tmp_head_nozzle_assignment = []
+            head_total_points = [0 for _ in range(config.head_num)]
+            for idx, nozzle_item in enumerate(nozzle_group):
+                if idx < config.head_num:
+                    tmp_head_nozzle_assignment.append([nozzle_item.copy()])
+                    head_total_points[idx] += nozzle_item[1]
+                else:
+                    min_head = np.argmin(head_total_points)
+                    tmp_head_nozzle_assignment[min_head].append(nozzle_item.copy())
+                    head_total_points[min_head] += nozzle_item[1]
+
+            cost = config.cycle_time * max(head_total_points)
+            for head in range(config.head_num):
+                for cycle in range(len(tmp_head_nozzle_assignment[head])):
+                    if cycle + 1 == len(tmp_head_nozzle_assignment[head]):
+                        if tmp_head_nozzle_assignment[head][cycle][0] != tmp_head_nozzle_assignment[head][-1][0]:
+                            cost += self.nozzle_change_weight
+                    else:
+                        if tmp_head_nozzle_assignment[head][cycle][0] != tmp_head_nozzle_assignment[head][cycle + 1][0]:
+                            cost += self.nozzle_change_weight
+
+            while True:
+                min_head, max_head = np.argmin(head_total_points), np.argmax(head_total_points)
+                min_head_nozzle, max_head_nozzle = tmp_head_nozzle_assignment[min_head][-1][0], \
+                                                   tmp_head_nozzle_assignment[max_head][-1][0]
+                if min_head_nozzle == max_head_nozzle:
+                    break
+
+                min_head_list, max_head_list = [min_head], [max_head]
+                minmax_head_points = 0
+                for head in range(config.head_num):
+                    if head in min_head_list or head in max_head_list:
+                        minmax_head_points += head_total_points[head]
+                        continue
+
+                    # the max/min heads with the sum nozzle type
+                    if tmp_head_nozzle_assignment[head][-1][0] == tmp_head_nozzle_assignment[min_head][-1][0]:
+                        min_head_list.append(head)
+                        minmax_head_points += head_total_points[head]
+
+                    if tmp_head_nozzle_assignment[head][-1][0] == tmp_head_nozzle_assignment[max_head][-1][0]:
+                        max_head_list.append(head)
+                        minmax_head_points += head_total_points[head]
+
+                # todo: restriction of available nozzle
+                # the reduction of cycles is not offset the cost of nozzle change
+                average_points = minmax_head_points // (len(min_head_list) + len(max_head_list))
+                reminder_points = minmax_head_points % (len(min_head_list) + len(max_head_list))
+                max_cycle = average_points + (1 if reminder_points > 0 else 0)
+                for head in range(config.head_num):
+                    if head in min_head_list or head in max_head_list:
+                        continue
+                    max_cycle = max(max_cycle, head_total_points[head])
+
+                nozzle_change_counter = 0
+                for head in min_head_list:
+                    if tmp_head_nozzle_assignment[head][0] == tmp_head_nozzle_assignment[head][-1]:
+                        nozzle_change_counter += 2
+                    else:
+                        nozzle_change_counter += 1
+
+                if self.cycle_weight * (max(head_total_points) - max_cycle) < self.nozzle_change_weight * nozzle_change_counter:
+                    break
+
+                cost -= self.cycle_weight * (max(head_total_points) - max_cycle) - self.nozzle_change_weight * nozzle_change_counter
+
+                required_points = 0  # 待均摊的贴装点数较多的吸嘴类型
+                for head in min_head_list:
+                    points = average_points - head_total_points[head]
+                    tmp_head_nozzle_assignment[head].append([max_head_nozzle, points])
+                    head_total_points[head] = average_points
+                    required_points += points
+
+                for head in max_head_list:
+                    tmp_head_nozzle_assignment[head][-1][1] -= required_points // len(max_head_list)
+                    head_total_points[head] -= required_points // len(max_head_list)
+
+                required_points -= (required_points // len(max_head_list)) * len(max_head_list)
+
+                for head in max_head_list:
+                    if required_points <= 0:
+                        break
+                    tmp_head_nozzle_assignment[head][-1][1] -= 1
+                    head_total_points[head] -= 1
+                    required_points -= 1
+
+            if min_cost is None or cost < min_cost:
+                min_cost = cost
+                head_nozzle_assignment = copy.deepcopy(tmp_head_nozzle_assignment)
+            else:
+                break
+
+            # 在吸嘴组中增加一个吸嘴
+            idx, nozzle = 0, nozzle_group[0][0]
+            for idx, [nozzle_, _] in enumerate(nozzle_group):
+                if nozzle_ != nozzle:
+                    break
+
+            average_points, remainder_points = nozzle_points[nozzle] // (idx + 1), nozzle_points[nozzle] % (idx + 1)
+            nozzle_group.append([nozzle, 0])
+            for idx, [nozzle_, _] in enumerate(nozzle_group):
+                if nozzle_ == nozzle:
+                    nozzle_group[idx][1] = average_points + (1 if remainder_points > 0 else 0)
+                    remainder_points -= 1
+
+        cycle_counter, nozzle_change_counter = 0, 0
+        for head in range(config.head_num):
+            head_cycle_counter = 0
+            for cycle in range(len(head_nozzle_assignment[head])):
+                if cycle + 1 == len(head_nozzle_assignment[head]):
+                    if head_nozzle_assignment[head][0][0] != head_nozzle_assignment[head][-1][0]:
+                        nozzle_change_counter += 1
+                else:
+                    if head_nozzle_assignment[head][cycle][0] != head_nozzle_assignment[head][cycle + 1][0]:
+                        nozzle_change_counter += 1
+                head_cycle_counter += head_nozzle_assignment[head][cycle][1]
+            cycle_counter = max(cycle_counter, head_cycle_counter)
+
+        # === 元件拾取次数预估 ===
+        cp_info = []
+        for idx, points in cp_points.items():
+            if points == 0:
+                continue
+            feeder_limit = 1  # todo: 暂时仅考虑一种吸嘴的情形
+            reminder_points = points % feeder_limit
+            for _ in range(feeder_limit):
+                cp_info.append([idx, points // feeder_limit + (1 if reminder_points > 0 else 0), cp_nozzle[idx]])
+                reminder_points -= 1
+
+        cp_info.sort(key=lambda x: -x[1])
+
+        nozzle_level, nozzle_counter = defaultdict(int), defaultdict(int)
+        level_points = defaultdict(int)
+        for info in cp_info:
+            nozzle = info[2]
+            if nozzle_counter[nozzle] and nozzle_counter[nozzle] % nozzle_heads[nozzle] == 0:
+                nozzle_level[nozzle] += 1
+            level = nozzle_level[nozzle]
+            level_points[level] = max(level_points[level], info[1])
+            nozzle_counter[nozzle] += 1
+
+        pickup_counter = sum(points for points in level_points.values())
+
+        return cycle_counter, nozzle_change_counter, anc_round_counter, pickup_counter
+
+    def encode(self, cp_points: defaultdict[str], cp_nozzle: defaultdict[str], board_width, board_height, config):
+        assert len(cp_points.keys()) == len(cp_nozzle.keys())
+
+        # === general info ===
+        total_points = sum(points for points in cp_points.values())
+        total_component_types, total_nozzle_types = len(cp_points.keys()), len(set(cp_nozzle.values()))
+
+        data = [total_points, total_component_types, total_nozzle_types]
+        data.extend([board_width, board_height])
+
+        # === heuristic info ===
+        cycle, nozzle_change, anc_move, pickup = self.subobjective(cp_points, cp_nozzle, config)
+        data.extend([cycle, nozzle_change, anc_move, pickup])
+
+        # === nozzle info ===
+        nozzle_points = defaultdict(int)
+        for cp_idx, nozzle in cp_nozzle.items():
+            nozzle_points[cp_nozzle[cp_idx]] += cp_points[cp_idx]       # points for different nozzle type
+        nozzle_items = [[nozzle, points] for nozzle, points in nozzle_points.items()]
+        nozzle_items = sorted(nozzle_items, key=lambda x: x[1], reverse=True)
+
+        nz2idx = defaultdict(int)
+        nozzle_slice = [0 for _ in range(self.max_nozzle_types)]
+        for idx, [nozzle, points] in enumerate(nozzle_items):
+            nz2idx[nozzle] = idx if idx < self.max_nozzle_types else self.max_nozzle_types - 1
+            nozzle_slice[idx if idx < self.max_nozzle_types else -1] += points
+        data.extend(nozzle_slice)
+
+        # === part info ===
+        part_data_slice = defaultdict(list)
+        for idx in range(self.max_nozzle_types):
+            part_data_slice[idx] = []
+
+        cp_items = [[component, points] for component, points in cp_points.items()]
+        cp_items = sorted(cp_items, key=lambda x: (x[1], nz2idx[cp_nozzle[x[0]]] * 0.1 + x[1]), reverse=True)
+        for component, points in cp_items:
+            nozzle = cp_nozzle[component]
+            part_data_slice[nz2idx[nozzle]].append(points)
+
+        data_slice = [0 for _ in range(self.max_nozzle_types)]
+        for idx, part_list in part_data_slice.items():
+            data_slice[idx] = len(part_list)
+        data.extend(data_slice)
+
+        for idx in range(self.max_nozzle_types):
+            if len(part_data_slice[idx]) <= self.max_component_types:
+                part_data_slice[idx].extend([0 for _ in range(self.max_component_types - len(part_data_slice[idx]))])
+            else:
+                part_data_slice[idx] = part_data_slice[idx][:self.max_component_types]
+            data.extend(part_data_slice[idx])
+
+        return data
+
+    def get_feature(self):
+        return (self.max_component_types + 2) * self.max_nozzle_types + 5 + 4
+
+    def eval(self, cp_points, cp_nozzle, board_width, board_height, config):
+        encoding = np.array(self.encode(cp_points, cp_nozzle, board_width, board_height, config))
+        encoding = torch.from_numpy(encoding.reshape((-1, np.shape(encoding)[0]))).float().to(self.device)
+        return self.net(encoding)[0, 0].item()
diff --git a/opt/smm/aggregation.py b/opt/smm/aggregation.py
new file mode 100644
index 0000000..c898f07
--- /dev/null
+++ b/opt/smm/aggregation.py
@@ -0,0 +1,172 @@
+from opt.smm.basis import *
+from opt.utils import *
+from opt.smm.solver import *
+
+
+class Aggregation(BaseOpt):
+    def __init__(self, config, part_data, step_data, feeder_data=pd.DataFrame(columns=['slot', 'part'])):
+        super().__init__(config, part_data, step_data, feeder_data)
+        self.feeder_assigner = FeederAssignOpt(config, part_data, step_data)
+
+    def optimize(self, hinter=True):
+        # === phase 0: data preparation ===
+        M = 1000  # a sufficient large number
+        a, b = 1, 6  # coefficient
+
+        part_list, nozzle_list = defaultdict(int), defaultdict(int)
+        cpidx_2_part, nzidx_2_nozzle = {}, {}
+        for _, data in self.step_data.iterrows():
+            part = data.part
+            if part not in cpidx_2_part.values():
+                cpidx_2_part[len(cpidx_2_part)] = part
+
+            part_list[part] += 1
+
+            idx = self.part_data[self.part_data['part'] == part].index.tolist()[0]
+            nozzle = self.part_data.loc[idx]['nz']
+            if nozzle not in nzidx_2_nozzle.values():
+                nzidx_2_nozzle[len(nzidx_2_nozzle)] = nozzle
+            nozzle_list[nozzle] += 1
+
+        I, J = len(part_list.keys()), len(nozzle_list.keys())  # the maximum number of part types and nozzle types
+        L = I + 1  # the maximum number of batch level
+        K = self.config.head_num  # the maximum number of heads
+        HC = [[M for _ in range(J)] for _ in range(I)]  # represent the nozzle-part compatibility
+
+        for i in range(I):
+            for _, item in enumerate(cpidx_2_part.items()):
+                index, part = item
+                cp_idx = self.part_data[self.part_data['part'] == part].index.tolist()[0]
+                nozzle = self.part_data.loc[cp_idx]['nz']
+
+                for j in range(J):
+                    if nzidx_2_nozzle[j] == nozzle:
+                        HC[index][j] = 0
+
+        # === phase 1: mathematical model solver ===
+        mdl = Model('SMT')
+        # mdl.setParam('OutputFlag', hinter)
+
+        # === Decision Variables ===
+        # the largest workload of all placement heads
+        WL = mdl.addVar(vtype=GRB.INTEGER, lb=0, ub=len(self.step_data), name='WL')
+
+        # the number of parts of type i that are placed by nozzle type j on placement head k
+        X = mdl.addVars(I, J, K, vtype=GRB.INTEGER, ub=max(part_list.values()), name='X')
+
+        # the total number of nozzle changes on placement head k
+        N = mdl.addVars(K, vtype=GRB.INTEGER, name='N')
+
+        # whether batch Xijk is placed on level l
+        Z = mdl.addVars(I, J, L, K, vtype=GRB.BINARY, name='Z')
+
+        # Dlk := 2 if a change of nozzles in the level l + 1 on placement head k
+        # Dlk := 1 if there are no batches placed on levels higher than l
+        # Dlk := 0 otherwise
+        D = mdl.addVars(L, K, vtype=GRB.BINARY, name='D')
+        D_plus = mdl.addVars(L, J, K, vtype=GRB.INTEGER, name='D_plus')
+        D_minus = mdl.addVars(L, J, K, vtype=GRB.INTEGER, name='D_minus')
+
+        # == Objective function ===
+        mdl.setObjective(a * WL + b * quicksum(N[k] for k in range(K)), GRB.MINIMIZE)
+
+        # === Constraint ===
+        mdl.addConstrs(
+            quicksum(X[i, j, k] for j in range(J) for k in range(K)) == part_list[cpidx_2_part[i]] for i in range(I))
+
+        mdl.addConstrs(quicksum(X[i, j, k] for i in range(I) for j in range(J)) <= WL for k in range(K))
+
+        mdl.addConstrs(
+            X[i, j, k] <= M * quicksum(Z[i, j, l, k] for l in range(L)) for i in range(I) for j in range(J) for k in
+            range(K))
+
+        mdl.addConstrs(
+            quicksum(Z[i, j, l, k] for l in range(L)) <= 1 for i in range(I) for j in range(J) for k in range(K))
+        mdl.addConstrs(
+            quicksum(Z[i, j, l, k] for l in range(L)) <= X[i, j, k] for i in range(I) for j in range(J) for k in
+            range(K))
+
+        mdl.addConstrs(quicksum(Z[i, j, l, k] for j in range(J) for i in range(I)) >= quicksum(
+            Z[i, j, l + 1, k] for j in range(J) for i in range(I)) for k in range(K) for l in range(L - 1))
+
+        mdl.addConstrs(
+            quicksum(Z[i, j, l, k] for i in range(I) for j in range(J)) <= 1 for k in range(K) for l in range(L))
+        mdl.addConstrs(D_plus[l, j, k] - D_minus[l, j, k] == quicksum(Z[i, j, l, k] for i in range(I)) - quicksum(
+            Z[i, j, l + 1, k] for i in range(I)) for l in range(L - 1) for j in range(J) for k in range(K))
+
+        mdl.addConstrs(
+            D[l, k] == quicksum((D_plus[l, j, k] + D_minus[l, j, k]) for j in range(J)) for k in range(K) for l in
+            range(L))
+
+        # mdl.addConstrs(2 * N[k] == quicksum(D[l, k] for l in range(L)) - 1 for k in range(K))
+        # mdl.addConstrs(
+        #     0 >= quicksum(HC[i][j] * Z[i, j, l, k] for i in range(I) for j in range(J)) for l in range(L) for k in
+        #     range(K))
+
+        # === Main Process ===
+        mdl.TimeLimit = 100
+        mdl.optimize()
+        if mdl.Status == GRB.OPTIMAL or mdl.Status == GRB.TIME_LIMIT:
+            print('total cost = {}'.format(mdl.objval))
+
+            # convert cp model solution to standard output
+            model_cycle_result, model_part_result = [], []
+            for l in range(L):
+                model_part_result.append([None for _ in range(K)])
+                model_cycle_result.append([0 for _ in range(K)])
+                for k in range(K):
+                    for i in range(I):
+                        for j in range(J):
+                            if abs(Z[i, j, l, k].x - 1) <= 1e-3:
+                                model_part_result[-1][k] = cpidx_2_part[i]
+                                model_cycle_result[-1][k] = round(X[i, j, k].x)
+
+                # remove redundant term
+                if sum(model_cycle_result[-1]) == 0:
+                    model_part_result.pop()
+                    model_cycle_result.pop()
+
+            head_part_index = [0 for _ in range(self.config.head_num)]
+            while True:
+                head_cycle = []
+                for head, index in enumerate(head_part_index):
+                    head_cycle.append(model_cycle_result[index][head])
+
+                if len([cycle for cycle in head_cycle if cycle > 0]) == 0:
+                    break
+
+                self.result.part.append([None for _ in range(self.config.head_num)])
+                min_cycle = min([cycle for cycle in head_cycle if cycle > 0])
+                for head, index in enumerate(head_part_index):
+                    if model_cycle_result[index][head] != 0:
+                        self.result.part[-1][head] = model_part_result[index][head]
+                    else:
+                        continue
+
+                    model_cycle_result[index][head] -= min_cycle
+                    if model_cycle_result[index][head] == 0 and index + 1 < len(model_cycle_result):
+                        head_part_index[head] += 1
+
+                self.result.cycle.append(min_cycle)
+
+            part_2_index = {}
+            for index, data in self.part_data.iterrows():
+                part_2_index[data['part']] = index
+
+            for cycle in range(len(self.result.part)):
+                for head in range(self.config.head_num):
+                    part = self.result.part[cycle][head]
+                    self.result.part[cycle][head] = -1 if part is None else part_2_index[part]
+
+            self.result.slot = self.feeder_assigner.do(self.result.part, self.result.cycle)
+            # === phase 2: heuristic method ===
+            self.result.point, self.result.sequence = self.path_planner.greedy_level_placing(self.result.part,
+                                                                                             self.result.cycle,
+                                                                                             self.result.slot)
+        else:
+            warnings.warn('No solution found!', UserWarning)
+
+
+
+
+
diff --git a/opt/smm/basis.py b/opt/smm/basis.py
new file mode 100644
index 0000000..8b65130
--- /dev/null
+++ b/opt/smm/basis.py
@@ -0,0 +1,363 @@
+from data.type import OptResult
+from opt.smm.path_plan import PathPlanOpt
+from collections import defaultdict
+
+import numpy as np
+import copy
+
+
+class BaseOpt:
+    def __init__(self, config, part_data, step_data, feeder_data=None):
+        self.part_data = part_data
+        self.step_data = step_data
+        self.feeder_data = feeder_data
+        self.config = config
+
+        self.result = OptResult()
+        self.path_planner = PathPlanOpt(config, part_data, step_data)
+
+        self.cycle_weight = 1
+        self.nozzle_change_weight = 1
+        self.pickup_weight = 1
+        self.place_weight = 1
+        self.move_weight = 1
+
+
+class FeederAssignOpt:
+    def __init__(self, config, part_data, step_data, feeder_data=None):
+        self.part_data = part_data
+        self.step_data = step_data
+        self.feeder_data = feeder_data
+        self.config = config
+
+    def find_commonpart(self, head_group, feeder_group):
+        feeder_group_len = len(feeder_group)
+
+        max_length, max_common_part = -1, []
+        for offset in range(-self.config.head_num + 1, feeder_group_len - 1):
+            # offset: head_group相对于feeder_group的偏移量
+            length, common_part = 0, []
+            for hd_index in range(self.config.head_num):
+                fd_index = hd_index + offset
+                if fd_index < 0 or fd_index >= feeder_group_len:
+                    common_part.append(-1)
+                    continue
+
+                if head_group[hd_index] == feeder_group[fd_index] and head_group[hd_index] != -1:
+                    length += 1
+                    common_part.append(head_group[hd_index])
+                else:
+                    common_part.append(-1)
+            if length > max_length:
+                max_length = length
+                max_common_part = common_part
+
+        return max_common_part
+
+    def do(self, part_result, cycle_result):
+        slot_result, feeder_group = [], []
+        feeder_limit = {idx: data.fdn for idx, data in self.part_data.iterrows()}
+
+        for part_cycle in part_result:
+            new_feeder_group = []
+            for part in part_cycle:
+                if part == -1 or feeder_limit[part] == 0 or new_feeder_group.count(part) >= feeder_limit[part]:
+                    new_feeder_group.append(-1)
+                else:
+                    new_feeder_group.append(part)
+
+            if len(new_feeder_group) == 0:
+                continue
+
+            while sum(i >= 0 for i in new_feeder_group) != 0:
+                max_common_part, index = [], -1
+                max_common_length = -1
+                for feeder_index in range(len(feeder_group)):
+                    common_part = self.find_commonpart(new_feeder_group, feeder_group[feeder_index])
+                    if sum(i > 0 for i in common_part) > max_common_length:
+                        max_common_length = sum(i > 0 for i in common_part)
+                        max_common_part, index = common_part, feeder_index
+
+                new_feeder_length = 0
+                for feeder in new_feeder_group:
+                    if feeder != -1 and feeder_limit[feeder] > 0:
+                        new_feeder_length += 1
+
+                if new_feeder_length > max_common_length:
+                    # 新分配供料器
+                    feeder_group.append([])
+                    for feeder_index in range(len(new_feeder_group)):
+                        feeder = new_feeder_group[feeder_index]
+                        if feeder != -1 and feeder_limit[feeder] > 0:
+                            feeder_group[-1].append(feeder)
+                            new_feeder_group[feeder_index] = -1
+                            feeder_limit[feeder] -= 1
+                        else:
+                            feeder_group[-1].append(-1)
+                else:
+                    # 使用旧供料器
+                    for feeder_index, feeder_part in enumerate(max_common_part):
+                        if feeder_part != -1:
+                            new_feeder_group[feeder_index] = -1
+
+        # 去除多余的元素
+        for group in feeder_group:
+            while len(group) > 0 and group[0] == -1:
+                group.pop(0)
+
+            while len(group) > 0 and group[-1] == -1:
+                group.pop(-1)
+
+        # 确定供料器组的安装位置
+        part_pos = defaultdict(list)
+        for _, data in self.step_data.iterrows():
+            idx = self.part_data[self.part_data['part'].values == data.part].index.tolist()[0]
+            part_pos[idx].append(data.x + self.config.stopper_pos.x)
+
+        # 元件使用的头
+        CT_Head = defaultdict(list)
+        for part_cycle in part_result:
+            for head, part in enumerate(part_cycle):
+                if part == -1:
+                    continue
+                if part not in CT_Head:
+                    CT_Head[part] = [head, head]
+                CT_Head[part][0] = min(CT_Head[part][0], head)
+                CT_Head[part][1] = max(CT_Head[part][1], head)
+
+        # 供料器组分配的优先顺序
+        feeder_assign_sequence = []
+        for i in range(len(feeder_group)):
+            for j in range(len(feeder_group)):
+                if j in feeder_assign_sequence:
+                    continue
+
+                if len(feeder_assign_sequence) == i:
+                    feeder_assign_sequence.append(j)
+                else:
+                    seq = feeder_assign_sequence[-1]
+                    if cycle_result[seq] * len([k for k in feeder_group[seq] if k >= 0]) < cycle_result[j] * len(
+                            [k for k in feeder_group[seq] if k >= 0]):
+                        feeder_assign_sequence.pop(-1)
+                        feeder_assign_sequence.append(j)
+
+        # TODO: 暂未考虑机械限位
+        feeder_group_slot = [-1] * len(feeder_group)
+        feeder_lane_state = [0] * self.config.slot_num  # 0表示空，1表示已占有
+        intv_ratio = self.config.head_intv // self.config.slot_intv
+        for index in feeder_assign_sequence:
+            group = feeder_group[index]
+            best_slot = []
+            for cp_index, part in enumerate(group):
+                if part == -1:
+                    continue
+                best_slot.append(round((sum(part_pos[part]) / len(part_pos[part]) - self.config.slotf1_pos.x)
+                                       / self.config.slot_intv) + 1 - cp_index * intv_ratio)
+            best_slot = round(sum(best_slot) / len(best_slot))
+
+            search_dir, step = 0, 0  # dir: 1-向右, 0-向左
+            left_out_range, right_out_range = False, False
+            while True:
+                assign_slot = best_slot + step if search_dir else best_slot - step
+                # 出现越界，反向搜索
+                if assign_slot + (len(group) - 1) * intv_ratio >= self.config.slot_num / 2:
+                    right_out_range = True
+                    search_dir = 0
+                    step += 1
+                elif assign_slot < 0:
+                    left_out_range = True
+                    search_dir = 1
+                    step += 1
+                else:
+                    if left_out_range or right_out_range:
+                        step += 1  # 单向搜索
+                    else:
+                        search_dir = 1 - search_dir  # 双向搜索
+                        if search_dir == 0:
+                            step += 1
+
+                assign_available = True
+
+                # === 分配对应槽位 ===
+                for slot in range(assign_slot, assign_slot + intv_ratio * len(group), intv_ratio):
+                    pick_part = group[(slot - assign_slot) // intv_ratio]
+                    if feeder_lane_state[slot] == 1 and pick_part != -1:
+                        assign_available = False
+                        break
+
+                    if pick_part != -1 and (slot - CT_Head[pick_part][0] * intv_ratio <= 0 or
+                                            slot + (self.config.head_num - CT_Head[pick_part][1] - 1) *
+                                            intv_ratio > self.config.slot_num // 2):
+                        assign_available = False
+                        break
+
+                if assign_available:
+                    for idx, part in enumerate(group):
+                        if part != -1:
+                            feeder_lane_state[assign_slot + idx * intv_ratio] = 1
+                    feeder_group_slot[index] = assign_slot
+                    break
+
+            if feeder_group_slot[index] == -1:
+                raise Exception('feeder assign error!')
+
+        # 按照最大匹配原则，确定各元件周期拾取槽位
+        for part_cycle in part_result:
+            slot_result.append([-1] * self.config.head_num)
+            head_index = [head for head, component in enumerate(part_cycle) if component >= 0]
+            while head_index:
+                max_overlap_counter = 0
+                overlap_feeder_group_index, overlap_feeder_group_offset = -1, -1
+                for index, group in enumerate(feeder_group):
+                    # offset 头1 相对于 供料器组第一个元件的偏移量
+                    for offset in range(-self.config.head_num + 1, self.config.head_num + len(group)):
+                        overlap_counter = 0
+                        for head in head_index:
+                            if 0 <= head + offset < len(group) and part_cycle[head] == group[head + offset]:
+                                overlap_counter += 1
+
+                        if overlap_counter > max_overlap_counter:
+                            max_overlap_counter = overlap_counter
+                            overlap_feeder_group_index, overlap_feeder_group_offset = index, offset
+
+                group = feeder_group[overlap_feeder_group_index]
+                head_index_cpy = copy.deepcopy(head_index)
+
+                for idx, head in enumerate(head_index_cpy):
+                    if 0 <= head + overlap_feeder_group_offset < len(group) and part_cycle[head] == \
+                            group[head + overlap_feeder_group_offset]:
+                        slot_result[-1][head] = feeder_group_slot[overlap_feeder_group_index] + intv_ratio * (
+                                                               head + overlap_feeder_group_offset)
+                        head_index.remove(head)
+
+        return slot_result
+
+
+class GenOpe:
+    @staticmethod
+    def roulette_wheel_selection(pop_eval):
+        random_val = np.random.random() * sum(pop_eval)
+        for idx, val in enumerate(pop_eval):
+            random_val -= val
+            if random_val <= 0:
+                return idx
+        return len(pop_eval) - 1
+
+    @staticmethod
+    def get_top_kth(pop_val, k: int, reverse=True):
+        res = []
+        pop_val_cpy = copy.deepcopy(pop_val)
+        pop_val_cpy.sort(reverse=reverse)
+
+        for i in range(min(len(pop_val_cpy), k)):
+            for j in range(len(pop_val)):
+                if abs(pop_val_cpy[i] - pop_val[j]) < 1e-9 and j not in res:
+                    res.append(j)
+                    break
+        return res
+
+    @staticmethod
+    def partially_mapped_crossover(parent1, parent2):
+        size = len(parent1)
+        start, end = sorted(np.random.randint(0, size, 2))
+
+        def create_child(primary_parent, secondary_parent):
+            child = [-1] * size
+            child[start:end + 1] = copy.deepcopy(secondary_parent[start:end + 1])
+
+            for i in range(size):
+                if start <= i <= end:
+                    continue
+
+                cur_ptr, cur_elem = 0, primary_parent[i]
+                while True:
+                    child[i] = cur_elem
+                    if child.count(cur_elem) == 1:
+                        break
+                    child[i] = -1
+
+                    if cur_ptr == 0:
+                        cur_ptr, cur_elem = 1, secondary_parent[i]
+                    else:
+                        index_ = child.index(cur_elem)
+                        cur_elem = secondary_parent[index_]
+
+            return child
+
+        return create_child(parent1, parent2), create_child(parent2, parent1)
+
+    @staticmethod
+    def swap_mutation(parent):
+        range_ = np.random.randint(0, len(parent), 2)
+        parent[range_[0]], parent[range_[1]] = parent[range_[1]], parent[range_[0]]
+        return parent
+
+    @staticmethod
+    def directed_edge_recombine_crossover(individual1, individual2):
+        assert len(individual1) == len(individual2)
+        left_edge_list, right_edge_list = defaultdict(list), defaultdict(list)
+
+        for index in range(len(individual1) - 1):
+            elem1, elem2 = individual1[index], individual1[index + 1]
+            right_edge_list[elem1].append(elem2)
+            left_edge_list[elem2].append(elem1)
+
+        for index in range(len(individual2) - 1):
+            elem1, elem2 = individual2[index], individual2[index + 1]
+            right_edge_list[elem1].append(elem2)
+            left_edge_list[elem2].append(elem1)
+
+        offspring = []
+        while len(offspring) != len(individual1):
+            while True:
+                center_element = np.random.choice(individual1)
+                if center_element not in offspring:  # 避免重复选取
+                    break
+            direction, candidate = 1, [center_element]
+            parent = center_element
+            for edge_list in left_edge_list.values():
+                while parent in edge_list:
+                    edge_list.remove(parent)
+
+            for edge_list in right_edge_list.values():
+                while parent in edge_list:
+                    edge_list.remove(parent)
+
+            while True:
+                max_len, max_len_neighbor = -1, 0
+                if direction == 1:
+                    if len(right_edge_list[parent]) == 0:
+                        direction, parent = -1, center_element
+                        continue
+                    for neighbor in right_edge_list[parent]:
+                        if max_len < len(right_edge_list[neighbor]):
+                            max_len_neighbor = neighbor
+                            max_len = len(right_edge_list[neighbor])
+                    candidate.append(max_len_neighbor)
+                    parent = max_len_neighbor
+                elif direction == -1:
+                    if len(left_edge_list[parent]) == 0:
+                        direction, parent = 0, center_element
+                        continue
+                    for neighbor in left_edge_list[parent]:
+                        if max_len < len(left_edge_list[neighbor]):
+                            max_len_neighbor = neighbor
+                            max_len = len(left_edge_list[neighbor])
+                    candidate.insert(0, max_len_neighbor)
+                    parent = max_len_neighbor
+                else:
+                    break
+
+                # 移除重复元素
+                for edge_list in left_edge_list.values():
+                    while max_len_neighbor in edge_list:
+                        edge_list.remove(max_len_neighbor)
+
+                for edge_list in right_edge_list.values():
+                    while max_len_neighbor in edge_list:
+                        edge_list.remove(max_len_neighbor)
+
+            offspring += candidate
+
+        return offspring
+
diff --git a/opt/smm/cell_division.py b/opt/smm/cell_division.py
new file mode 100644
index 0000000..df952a6
--- /dev/null
+++ b/opt/smm/cell_division.py
@@ -0,0 +1,237 @@
+from opt.smm.basis import *
+from opt.utils import *
+
+
+class CellDivisionOpt(BaseOpt):
+    def __init__(self, config, part_data, step_data, feeder_data=None):
+        super().__init__(config, part_data, step_data, feeder_data)
+
+        self.feeder_assigner = FeederAssignOpt(config, part_data, step_data)
+
+        self.e_gang_pick = 0.6
+        self.e_nz_change = 4
+
+    def evaluate(self, part_result, cycle_result, slot_result) -> float:
+        nozzle_change_counter = 0
+        for head in range(self.config.head_num):
+            nozzle = ''
+            for cycle in range(len(part_result)):
+                component_index = part_result[cycle][head]
+                if component_index == -1:
+                    continue
+
+                if cycle != 0 and nozzle != self.part_data.loc[component_index, 'nz']:
+                    nozzle_change_counter += 1
+                nozzle = self.part_data.loc[component_index, 'nz']
+
+        gang_pick_counter = 0
+        for cycle, feeder_slot in enumerate(slot_result):
+            pick_slot = defaultdict(int)
+            for head, slot in enumerate(feeder_slot):
+                if slot == -1:
+                    continue
+                pick_slot[slot - head * (self.config.head_intv / self.config.slot_intv)] += 1
+            for _ in pick_slot.values():
+                gang_pick_counter += cycle_result[cycle]
+
+        return sum(cycle_result) + self.e_nz_change * nozzle_change_counter + self.e_gang_pick * gang_pick_counter
+
+    def convertor(self, part_cell, population):
+        assert part_cell['points'].sum() == len(self.step_data)
+        head_num = self.config.head_num
+        head_assignment = [[] for _ in range(head_num)]
+
+        wl = [0 for _ in range(head_num)]  # workload
+
+        e1, e2, e3 = 1, 2, 1. / 6
+
+        part_result, cycle_result, feeder_slot_result = [], [], []
+        for index in population:
+            if part_cell.loc[index]['points'] == 0:
+                continue
+            # 鍏冭優瀵瑰簲鐨勫厓浠剁被鍨嬪拰璐磋鐐规暟
+            part_type, part_points = int(part_cell.loc[index, 'index']), int(part_cell.loc[index, 'points'])
+
+            nozzle_change, maxwl = [0 for _ in range(head_num)], [0 for _ in range(head_num)]
+            for head in range(head_num):
+                if head_assignment[head]:
+                    assigned_part = head_assignment[head][-1][0]
+                    if self.part_data.loc[assigned_part]['nz'] != self.part_data.loc[part_type]['nz']:
+                        nozzle_change[head] = 1
+                wl1 = wl.copy()
+                wl1[head] += part_points
+                maxwl[head] = max(wl1) + e1 * nozzle_change[head]
+
+            awl, wl2 = min(maxwl), wl.copy()
+            for idx, val in enumerate(maxwl):
+                if val > awl:
+                    wl2[idx] += e3
+            head_ = wl2.index(min(wl2))
+            wl[head_] += part_points
+            head_assignment[head_].append([part_type, part_points])
+
+        head_assignment_counter = [0 for _ in range(head_num)]
+        while True:
+            assigned_part, assigned_cycle = [-1 for _ in range(head_num)], [0 for _ in range(head_num)]
+            for head in range(head_num):
+                counter = head_assignment_counter[head]
+
+                if head_assignment[head] and head_assignment[head][counter][1] > 0:
+                    assigned_part[head] = head_assignment[head][counter][0]
+                    assigned_cycle[head] = head_assignment[head][counter][1]
+
+            nonzero_cycle = [cycle for cycle in assigned_cycle if cycle > 0]
+            if not nonzero_cycle:
+                break
+
+            cycle_result.append(min(nonzero_cycle))
+            part_result.append(assigned_part)
+
+            for head in range(head_num):
+                counter = head_assignment_counter[head]
+
+                if head_assignment[head] and head_assignment[head][counter][1] > 0:
+                    head_assignment[head][counter][1] -= cycle_result[-1]
+                    if head_assignment[head][counter][1] == 0 and counter < len(head_assignment[head]) - 1:
+                        head_assignment_counter[head] += 1
+
+        slot_result = self.feeder_assigner.do(part_result, cycle_result)
+        return part_result, cycle_result, slot_result
+
+    def optimize(self, hinter=True):
+        # Crossover method: Two-point crossover
+        # Mutation method: Swap
+        # Parent selection method: Roulette wheel
+        # Termination condition: 20 successive non-improvement iterations
+        population_size = 40  # 绉嶇兢瑙勬ā
+        crossover_rate, mutation_rate = .6, .02
+        golden_section = 0.618
+
+        # 鑾峰彇鍏冧欢鍏冭優
+        part_points = defaultdict(int)
+        for _, data in self.step_data.iterrows():
+            part_points[data.part] += 1
+        feeder_num = sum(self.part_data['fdn'])
+        part_cell = pd.DataFrame({'index': np.arange(feeder_num), 'points': np.zeros(feeder_num, dtype=int)})
+        cell_index = 0
+        for part_index, data in self.part_data.iterrows():
+            total_points, div_points = part_points[data.part], math.ceil(part_points[data.part] / data.fdn)
+            for _ in range(data.fdn):
+                part_cell.loc[cell_index, 'index'] = part_index
+                part_cell.loc[cell_index, 'points'] = min(div_points, total_points)
+                total_points -= div_points
+                cell_index += 1
+
+        part_cell = part_cell[~part_cell['points'].isin([0])]
+
+        # part_cell.sort_values(by = "points" , inplace = True, ascending = False)
+        best_population, best_part_cell = [], []
+        min_pop_val = float('inf')  # 鏈�浼樼缇や环鍊�
+        Div, Imp = 0, 0
+        while True:
+            # randomly generate permutations
+            generation_ = np.array(part_cell.index)
+            pop_generation = []
+            for _ in range(population_size):
+                np.random.shuffle(generation_)
+                pop_generation.append(generation_.tolist())
+
+            pop_val = []
+            for pop in range(population_size):
+                part_result, cycle_result, slot_result = self.convertor(part_cell, pop_generation[pop])
+                pop_val.append(self.evaluate(part_result, cycle_result, slot_result))
+
+            # 鍒濆鍖栭殢鏈虹敓鎴愮缇�
+            Upit = int(1.5 * np.sqrt(len(part_cell)))
+
+            while Div < Upit:
+                if hinter:
+                    print('----- current div :   ' + str(Div) + ' , total div :   ' + str(Upit) + '  -----')
+
+                # 閫夋嫨
+                new_pop_generation, new_pop_val = [], []
+                top_k_index = GenOpe.get_top_kth(pop_val, int(population_size * 0.3))
+                for index in top_k_index:
+                    new_pop_generation.append(pop_generation[index])
+                    new_pop_val.append(pop_val[index])
+                index = [i for i in range(population_size)]
+
+                select_index = random.choices(index, weights=pop_val, k=population_size - int(population_size * 0.3))
+                for index in select_index:
+                    new_pop_generation.append(pop_generation[index])
+                    new_pop_val.append(pop_val[index])
+                pop_generation, pop_val = new_pop_generation, new_pop_val
+
+                # 浜ゅ弶
+                for pop in range(population_size):
+                    if pop % 2 == 0 and np.random.random() < crossover_rate:
+                        index1, index2 = GenOpe.roulette_wheel_selection(pop_val), -1
+                        while True:
+                            index2 = GenOpe.roulette_wheel_selection(pop_val)
+                            if index1 != index2:
+                                break
+                        # 涓ょ偣浜ゅ弶绠楀瓙
+                        pop_generation[index1], pop_generation[index2] = GenOpe.partially_mapped_crossover(pop_generation[index1],
+                                                                                                           pop_generation[index2])
+
+                    if np.random.random() < mutation_rate:
+                        index_ = GenOpe.roulette_wheel_selection(pop_val)
+                        GenOpe.swap_mutation(pop_generation[index_])
+
+                # 灏嗗厓浠跺厓鑳炲垎閰嶅埌鍚勪釜鍚告潌涓婏紝璁＄畻浠峰�煎嚱鏁�
+                for pop in range(population_size):
+                    part_result, cycle_result, slot_result = self.convertor(part_cell, pop_generation[pop])
+                    pop_val[pop] = self.evaluate(part_result, cycle_result, slot_result)
+
+                    assert pop_val[pop] > 0
+
+                if min(pop_val) < min_pop_val:
+                    min_pop_val = min(pop_val)
+                    best_population = copy.deepcopy(pop_generation[np.argmin(pop_val)])
+                    best_part_cell = copy.deepcopy(part_cell)
+                    Div, Imp = 0, 1
+                else:
+                    Div += 1
+
+            if Imp == 1:
+                Div, Imp = 0, 0
+                # Section: cell division operation
+                if hinter:
+                    print(' -------------  cell division operation  ------------- ')
+                div_part_cell = pd.DataFrame()
+                for idx, rows in part_cell.iterrows():
+                    if part_cell.loc[idx, 'points'] <= 1:
+                        div_part_cell = pd.concat([div_part_cell, pd.DataFrame([rows])], ignore_index=True)
+                    else:
+                        div_part_cell = pd.concat([div_part_cell, pd.DataFrame([rows] * 2)], ignore_index=True)
+
+                        rows_counter = len(div_part_cell)
+                        div_points = int(max(np.ceil(div_part_cell.loc[rows_counter - 2, 'points'] * golden_section), 1))
+
+                        # 閬垮厤鍑虹幇绌哄厓鑳炵殑鎯呭舰
+                        if div_points == 0 or div_points == div_part_cell.loc[rows_counter - 2, 'points']:
+                            div_part_cell.loc[rows_counter - 2, 'points'] = 1
+                        else:
+                            div_part_cell.loc[rows_counter - 2, 'points'] = div_points
+
+                        div_part_cell.loc[rows_counter - 1, 'points'] -= div_part_cell.loc[rows_counter - 2, 'points']
+
+                        if div_part_cell.loc[rows_counter - 2, 'points'] == 0 or \
+                                div_part_cell.loc[rows_counter - 1, 'points'] == 0:
+                            raise ValueError
+
+                part_cell = div_part_cell
+
+                # 瀹屾垚鍒嗚鍚庨噸鏂扮敓鎴愭煋鑹蹭綋缁�
+                generation_ = np.array(range(len(part_cell)))
+                pop_generation = []
+                for _ in range(population_size):
+                    np.random.shuffle(generation_)
+                    pop_generation.append(generation_.tolist())
+            else:
+                break
+
+        assert len(best_part_cell) == len(best_population)
+        self.result.part, self.result.cycle, self.result.slot = self.convertor(best_part_cell, best_population)
+        self.result.point, self.result.sequence = self.path_planner.scan_based(self.result.part,
+                                                                               self.result.cycle, self.result.slot)
diff --git a/opt/smm/feeder_priority.py b/opt/smm/feeder_priority.py
new file mode 100644
index 0000000..38b6940
--- /dev/null
+++ b/opt/smm/feeder_priority.py
@@ -0,0 +1,791 @@
+from opt.smm.basis import *
+from opt.utils import *
+
+
+class FeederPriorityOpt(BaseOpt):
+    def __init__(self, config, part_data, step_data, feeder_data=pd.DataFrame(columns=['slot', 'part'])):
+        super().__init__(config, part_data, step_data, feeder_data)
+
+        self.e_gang_pick = 0.6
+        self.e_nz_change = 4
+
+    def optimize(self, hinter=True):
+        self.feeder_priority_assignment(hinter=hinter)
+        self.result.point, self.result.sequence = self.path_planner.scan_based(self.result.part, self.result.cycle,
+                                                                               self.result.slot)
+
+    def feeder_priority_assignment(self, hinter=True):
+        feeder_allocate_val = np.inf
+        nozzle_pattern_list = self.feeder_nozzle_pattern()
+        pbar = tqdm(total=len(nozzle_pattern_list), desc='feeder priority process') if hinter else None
+
+        # 绗�1姝ワ細纭畾鍚稿槾鍒嗛厤妯″紡
+        allocated_feeder_data = copy.deepcopy(self.feeder_data)
+        for nozzle_pattern in nozzle_pattern_list:
+            feeder_data = copy.deepcopy(allocated_feeder_data)
+            # 绗�2姝ワ細鍒嗛厤渚涙枡鍣ㄤ綅缃�
+            self.feeder_allocate(feeder_data, nozzle_pattern, figure=False)
+            # 绗�3姝ワ細鎵弿渚涙枡鍣ㄥ熀搴э紝纭畾鍏冧欢鎷惧彇鐨勫厛鍚庨『搴�
+            result = OptResult()
+            result.part, result.cycle, result.slot = self.feeder_base_scan(feeder_data)
+            info = evaluation(self.config, self.part_data, self.step_data, result)
+            val = self.cycle_weight * info.cycle_counter + self.nozzle_change_weight * info.nozzle_change_counter + \
+                  self.pickup_weight * info.pickup_counter + self.move_weight * info.pickup_distance
+            if val < feeder_allocate_val:
+                feeder_allocate_val = val
+                self.result, self.feeder_data = result, feeder_data
+            if pbar:
+                pbar.update(1)
+
+        return self.result.part, self.result.cycle, self.result.slot
+
+    def feeder_nozzle_pattern(self):
+        nozzle_pattern_list = []
+        nozzle_points = defaultdict(int)
+        head_num = self.config.head_num
+
+        part_nozzle = defaultdict(str)
+        for _, data in self.part_data.iterrows():
+            part_nozzle[data.part] = data.nz
+
+        for _, data in self.step_data.iterrows():
+            nozzle_points[part_nozzle[data.part]] += 1
+
+        while len(nozzle_points.keys()) > head_num:
+            del nozzle_points[min(nozzle_points.items(), key=lambda x: x[1])[0]]
+
+        sum_points = sum(nozzle_points.values())
+        nozzle_points = defaultdict(int, {k: v for k, v in nozzle_points.items() if v / sum_points >= 0.8 / head_num})
+
+        head_assign_indexes = [int(head_num // 2 + pow(-1, h + 1) * (math.ceil(h / 2) - 1 / 2) +
+                               math.ceil((head_num + 1) % 2) / 2) - 1 for h in range(1, head_num + 1)]
+        while len(nozzle_points):
+            nozzle_heads, nozzle_indices = defaultdict(int), defaultdict(str),
+            min_points_nozzle = None
+            for idx, (nozzle, points) in enumerate(nozzle_points.items()):
+                nozzle_heads[nozzle], nozzle_indices[idx] = 1, nozzle
+                if min_points_nozzle is None or points < nozzle_points[min_points_nozzle]:
+                    min_points_nozzle = nozzle
+
+            while sum(nozzle_heads.values()) != head_num:
+                max_cycle = None
+
+                for nozzle, head_cnt in nozzle_heads.items():
+                    if max_cycle is None or nozzle_points[nozzle] / head_cnt > nozzle_points[max_cycle] / \
+                            nozzle_heads[max_cycle]:
+                        max_cycle = nozzle
+                    elif nozzle_points[nozzle] / head_cnt == nozzle_points[max_cycle] / nozzle_heads[max_cycle]:
+                        if head_cnt > nozzle_heads[max_cycle]:
+                            max_cycle = nozzle
+
+                assert max_cycle is not None
+                nozzle_heads[max_cycle] += 1
+
+            num_permu = reduce(lambda x, y: x * y, range(1, len(nozzle_indices.keys()) + 1))
+            num_permu = num_permu // 2 if len(nozzle_indices.keys()) > 3 else num_permu
+            for permu in itertools.permutations(nozzle_indices.keys()):
+                if (num_permu := num_permu - 1) < 0:
+                    break
+                nozzle_pattern_list.append([])
+                for idx in permu:
+                    for _ in range(nozzle_heads[nozzle_indices[idx]]):
+                        nozzle_pattern_list[-1].append(nozzle_indices[idx])
+
+            if len(nozzle_points.keys()) > 1:
+                nozzle_average_points = []
+                for nozzle, head in nozzle_heads.items():
+                    nozzle_average_points.append([nozzle, head, nozzle_points[nozzle] / head])
+
+                nozzle_average_points = sorted(nozzle_average_points, key=lambda x: -x[2])
+                idx = 0
+                nozzle_pattern_list.append(['' for _ in range(head_num)])
+                for nozzle, head, _ in nozzle_average_points:
+                    for _ in range(head):
+                        nozzle_pattern_list[-1][head_assign_indexes[idx]] = nozzle
+                        idx += 1
+
+                idx = 1
+                nozzle_pattern_list.append(['' for _ in range(head_num)])
+                for nozzle, head, _ in nozzle_average_points:
+                    for _ in range(head):
+                        nozzle_pattern_list[-1][head_assign_indexes[-idx]] = nozzle
+                        idx += 1
+
+            nozzle_points.pop(min_points_nozzle)
+        return nozzle_pattern_list
+
+    def feeder_allocate(self, feeder_data, nozzle_pattern, figure=False):
+        head_num, slot_num = self.config.head_num, self.config.slot_num
+        slot_intv, head_intv = self.config.slot_intv, self.config.head_intv
+        intv_ratio = round(head_intv / slot_intv)
+        feeder_points, feeder_division_points = defaultdict(int), defaultdict(int)   # 渚涙枡鍣ㄨ创瑁呯偣鏁�
+        feeder_center_pos = defaultdict(float)
+
+        feeder_limit, feeder_arrange = defaultdict(int), defaultdict(int)
+        part_nozzle = defaultdict(str)
+
+        feeder_base = [-2] * slot_num   # 宸插畨瑁呭湪渚涙枡鍣ㄥ熀搴т笂鐨勫厓浠讹紙-2: 鏈垎閰嶏紝-1: 鍗犵敤鐘舵�侊級
+        feeder_base_points = [0] * slot_num   # 渚涙枡鍣ㄥ熀搴х粨浣欒创瑁呯偣鏁伴噺
+        part_index = defaultdict(int)
+        for idx, data in self.part_data.iterrows():
+            part_index[data.part] = idx
+
+            feeder_limit[idx] = data.fdn
+            feeder_arrange[idx] = 0
+
+        for _, data in self.step_data.iterrows():
+            pos, part = data.x + self.config.stopper_pos.x, data.part
+
+            index = part_index[part]
+
+            feeder_points[index] += 1
+            feeder_center_pos[index] += ((pos - feeder_center_pos[index]) / feeder_points[index])
+            part_nozzle[index] = self.part_data.loc[index].nz
+
+        for index, points in feeder_points.items():
+            feeder_division_points[index] = points // feeder_limit[index]
+
+        nozzle_part, nozzle_part_points = defaultdict(list), defaultdict(list)
+        for part, nozzle in part_nozzle.items():
+            for _ in range(feeder_limit[part]):
+                nozzle_part[nozzle].append(part)
+                nozzle_part_points[nozzle].append(feeder_points[part])
+
+        if feeder_data is not None:
+            for _, feeder in feeder_data.iterrows():
+                slot, part = feeder.slot, feeder.part
+                index = part_index[part]
+
+                # 渚涙枡鍣ㄥ熀搴у垎閰嶄綅缃拰瀵瑰簲璐磋鐐规暟
+                feeder_base[slot], feeder_base_points[slot] = index, feeder_division_points[index]
+
+                feeder_type = self.part_data.loc[index].fdr
+                extra_width = feeder_width[feeder_type][0] + feeder_width[feeder_type][1] - slot_intv
+                while extra_width > 0:
+                    slot += 1
+                    feeder_base[slot] = -1
+                    extra_width -= slot_intv
+
+                feeder_limit[index] -= 1
+                feeder_arrange[index] += 1
+                if feeder_limit[index] < 0:
+                    info = 'the number of arranged feeder for [' + part + '] exceeds the quantity limit'
+                    raise ValueError(info)
+
+                for nozzle, part in nozzle_part.items():
+                    if index in part:
+                        index_ = part.index(index)
+
+                        nozzle_part[nozzle].pop(index_)
+                        nozzle_part_points[nozzle].pop(index_)
+                        break
+
+        head_assign_indexes = [int(head_num // 2 + pow(-1, h + 1) * (math.ceil(h / 2) - 1 / 2) +
+                               math.ceil((head_num + 1) % 2) / 2) - 1 for h in range(1, head_num + 1)]
+        assert len(nozzle_pattern) == head_num
+        while True:
+            best_assign, best_assign_points = [], []
+            best_assign_slot, best_assign_value = -1, -np.inf
+            best_nozzle_part, best_nozzle_part_points = None, None
+            for slot in range(1, slot_num // 2 - (head_num - 1) * intv_ratio + 1):
+                feeder_assign, feeder_assign_points = [], []
+                tmp_feeder_limit, tmp_feeder_points = feeder_limit.copy(), feeder_points.copy()
+                tmp_nozzle_part, tmp_nozzle_part_points = copy.deepcopy(nozzle_part), copy.deepcopy(
+                    nozzle_part_points)
+
+                # 璁板綍鎵弿鍒扮殑宸插畨瑁呯殑渚涙枡鍣ㄥ厓浠剁被鍨�
+                for head in range(head_num):
+                    feeder_assign.append(feeder_base[slot + head * intv_ratio])
+
+                    if feeder_assign[-1] >= 0:
+                        feeder_assign_points.append(feeder_base_points[slot + head * intv_ratio])
+                        if feeder_assign_points[-1] <= 0:
+                            feeder_assign[-1], feeder_assign_points[-1] = -1, 0
+                    else:
+                        feeder_assign_points.append(0)
+
+                if -2 not in feeder_assign:
+                    continue
+
+                assign_part_stack, assign_part_stack_points = [], []
+                for idx in head_assign_indexes:
+                    if feeder_assign[idx] != -2:
+                        continue
+
+                    # 鍚稿槾鍖归厤妯″紡闈炵┖锛屾寜瀵瑰簲鍚稿槾绫诲瀷杩涜鍏冧欢鍒嗛厤
+                    nozzle_assign = nozzle_pattern[idx]
+
+                    if len(tmp_nozzle_part[nozzle_assign]) == 0:
+                        # 褰撳墠澶村搴斿惛鍢寸被鍨嬫棤鍙敤鍏冧欢锛屽皢璁″垝鍒嗛厤鐨勫厓浠跺帇鍏ュ爢鏍�
+                        part = max(tmp_feeder_points.keys(),
+                                   key=lambda x: tmp_feeder_points[x] / tmp_feeder_limit[x]
+                                   if tmp_feeder_limit[x] != 0 else 0)
+                        for nozzle, part_list in tmp_nozzle_part.items():
+                            if part in part_list:
+                                nozzle_assign = nozzle
+
+                                assign_part_stack.append(part)
+                                assign_part_stack_points.append(feeder_division_points[part])
+                                break
+                    else:
+                        # 褰撳墠澶村搴斿惛鍢寸被鍨嬫湁鍙敤鍏冧欢锛岀洿鎺ュ垎閰嶅搴旂被鍨嬬殑鍏冧欢
+                        index_ = tmp_nozzle_part[nozzle_assign].index(max(tmp_nozzle_part[nozzle_assign],
+                                                                          key=lambda x: tmp_feeder_points[x] /
+                                                                                             tmp_feeder_limit[x] if
+                                                                               tmp_feeder_limit[x] != 0 else 0))
+
+                        part = tmp_nozzle_part[nozzle_assign][index_]
+
+                        feeder_type = self.part_data.loc[part].fdr
+                        extra_width, extra_slot = feeder_width[feeder_type][0] + feeder_width[feeder_type][1] - slot_intv, 1
+                        slot_overlap = False
+                        while extra_width > 0:
+                            slot_ = slot + idx * intv_ratio + extra_slot
+                            if feeder_base[slot_] != -2 or slot_ > slot_num // 2:
+                                slot_overlap = True
+                                break
+                            if idx + extra_slot // 2 < head_num and feeder_assign[idx + extra_slot // 2] >= 0:
+                                slot_overlap = True
+                                break
+                            extra_width -= slot_intv
+                            extra_slot += 1
+
+                        # 鍙敤渚涙枡鍣ㄦ暟鐩厖瓒充笖涓嶅瓨鍦ㄥ拰宸叉湁渚涙枡鍣ㄧ殑鍗犱綅鍐茬獊
+                        if tmp_feeder_limit[part] > 0 and not slot_overlap:
+                            feeder_assign[idx], feeder_assign_points[idx] = part, feeder_division_points[part]
+                            extra_width, extra_head = feeder_width[feeder_type][0] + feeder_width[feeder_type][
+                                1] - head_intv, 1
+                            while extra_width > 0 and idx + extra_head < head_num:
+                                feeder_assign[idx + extra_head] = -1
+                                extra_head += 1
+                                extra_width -= head_intv
+                        else:
+                            part = -1       # 瀛樺湪浣嶇疆鍐茬獊鐨勫厓浠讹紝涓嶅崰鐢ㄥ彲鐢ㄤ緵鏂欏櫒鏁�
+
+                    if part >= 0 and tmp_feeder_limit[part] == 0:
+                        continue
+
+                    if part in tmp_nozzle_part[nozzle_assign]:
+                        index = tmp_nozzle_part[nozzle_assign].index(part)
+
+                        tmp_nozzle_part[nozzle_assign].pop(index)
+                        tmp_nozzle_part_points[nozzle_assign].pop(index)
+
+                        tmp_feeder_limit[part] -= 1
+                        tmp_feeder_points[part] -= feeder_division_points[part]
+
+                # 鍏冧欢鍫嗘爤鍑烘爤锛岄鍏堝垎閰嶅惛鍢寸被鍨嬩竴鑷寸殑澶�
+                if nozzle_pattern:
+                    for head, feeder in enumerate(feeder_assign):
+                        if feeder != -2:
+                            continue
+                        for idx, part in enumerate(assign_part_stack):
+                            feeder_type = self.part_data.loc[part].fdr
+                            extra_width, extra_slot = feeder_width[feeder_type][0] + feeder_width[feeder_type][
+                                1] - slot_intv, 1
+
+                            slot_overlap = False
+                            while extra_width > 0:
+                                slot_ = slot + head * intv_ratio + extra_slot
+                                if feeder_base[slot_] != -2 or slot_ > slot_num // 2:
+                                    slot_overlap = True
+                                    break
+                                extra_width -= slot_intv
+                                extra_slot += 1
+
+                            if self.part_data.loc[part].nz == nozzle_pattern[head] and not slot_overlap:
+                                feeder_assign[head], feeder_assign_points[head] = assign_part_stack[idx], \
+                                                                                  assign_part_stack_points[idx]
+
+                                assign_part_stack.pop(idx)
+                                assign_part_stack_points.pop(idx)
+                                break
+
+                # 鍏冧欢鍫嗘爤锛岀劧鍚庡垎閰嶅厓浠跺爢鏍堜腑鏈垎閰嶇殑鍏跺畠鍏冧欢
+                for head in head_assign_indexes:
+                    if feeder_assign[head] != -2 or len(assign_part_stack) == 0:
+                        continue
+                    part, points = assign_part_stack[0], assign_part_stack_points[0]
+
+                    feeder_type = self.part_data.loc[part].fdr
+                    extra_width = feeder_width[feeder_type][0] + feeder_width[feeder_type][1] - slot_intv
+                    extra_slot = 1
+
+                    slot_overlap = False
+                    while extra_width > 0:
+                        slot_ = slot + head * intv_ratio + extra_slot
+                        if feeder_base[slot_] != -2 or slot_ > slot_num // 2:
+                            slot_overlap = True
+                            break
+                        extra_width -= slot_intv
+                        extra_slot += 1
+
+                    if not slot_overlap:
+                        feeder_assign[head], feeder_assign_points[head] = part, points
+                        extra_width = feeder_width[feeder_type][0] + feeder_width[feeder_type][1] - head_intv
+                        extra_head = 1
+                        while extra_width > 0 and head + extra_head < head_num:
+                            feeder_assign[head + extra_head] = -1
+                            extra_head += 1
+                            extra_width -= head_intv
+                    else:
+                        # 杩旇繕鐢变簬鏈烘闄愪綅鏃犳硶鍒嗛厤鐨勶紝鍘嬪叆鍏冧欢鍫嗘爤涓殑鍏冪礌
+                        nozzle = self.part_data.loc[part].nz
+                        tmp_nozzle_part[nozzle].insert(0, part)
+                        tmp_nozzle_part_points[nozzle].insert(0, points)
+
+                    assign_part_stack.pop(0)
+                    assign_part_stack_points.pop(0)
+
+                # 浠嶇劧瀛樺湪鐢变簬鏈烘闄愪綅锛屾棤娉曡繘琛屽垎閰嶇殑鍦ㄥ爢鏍堜腑鐨勫厓浠�
+                while assign_part_stack:
+                    part, points = assign_part_stack[0], assign_part_stack_points[0]
+                    nozzle = self.part_data.loc[part].nz
+
+                    tmp_nozzle_part[nozzle].insert(0, part)
+                    tmp_nozzle_part_points[nozzle].insert(0, points)
+
+                    assign_part_stack.pop(0)
+                    assign_part_stack_points.pop(0)
+
+                nozzle_change_counter = 0
+                average_slot, average_head = [], []
+                for head, feeder_ in enumerate(feeder_assign):
+                    if feeder_ < 0:
+                        continue
+                    average_slot.append((feeder_center_pos[feeder_] - self.config.slotf1_pos.x) / slot_intv + 1)
+                    average_head.append(head)
+                    if nozzle_pattern and self.part_data.loc[feeder_].nz != nozzle_pattern[head]:
+                        nozzle_change_counter += 1
+
+                if len(average_slot) == 0:
+                    continue
+
+                average_slot = sum(average_slot) / len(average_slot) - sum(average_head) / len(average_head) * intv_ratio
+                assign_value = 0
+                feeder_assign_points_cpy = feeder_assign_points.copy()
+                while True:
+                    points_filter = list(filter(lambda x: x > 0, feeder_assign_points_cpy))
+                    if not points_filter:
+                        break
+                    assign_value += self.e_gang_pick * min(points_filter) * (len(points_filter) - 1)
+                    for head, _ in enumerate(feeder_assign_points_cpy):
+                        if feeder_assign_points_cpy[head] == 0:
+                            continue
+                        feeder_assign_points_cpy[head] -= min(points_filter)
+
+                assign_value -= (1e2 * self.e_nz_change * nozzle_change_counter + 1e-5 * abs(slot - average_slot))
+
+                if assign_value >= best_assign_value and sum(feeder_assign_points) != 0:
+
+                    best_assign_value = assign_value
+                    best_assign = feeder_assign.copy()
+                    best_assign_points = feeder_assign_points.copy()
+                    best_assign_slot = slot
+                    best_nozzle_part, best_nozzle_part_points = \
+                        tmp_nozzle_part.copy(), tmp_nozzle_part_points.copy()
+
+            if not best_assign_points:
+                break
+
+            for idx, part in enumerate(best_assign):
+                if part < 0:
+                    continue
+                # 鏂板畨瑁呯殑渚涙枡鍣�
+                if feeder_base[best_assign_slot + idx * intv_ratio] != part:
+                    # 闄ゅ幓鍒嗛厤缁欐渶澶у寲鍚屾椂鎷惧彇鍛ㄦ湡鐨勯」锛屼繚鐣欑粨浣欓」
+                    feeder_base_points[best_assign_slot + idx * intv_ratio] += (
+                                feeder_division_points[part] - min(filter(lambda x: x > 0, best_assign_points)))
+
+                    feeder_points[part] -= feeder_division_points[part]
+                    feeder_limit[part] -= 1
+                    feeder_arrange[part] += 1
+
+                    if feeder_limit[part] == 0:
+                        feeder_division_points[part] = 0
+                        for nozzle, part_list in nozzle_part.items():
+                            if part in part_list:
+                                index_ = part_list.index(part)
+
+                                nozzle_part[nozzle].pop(index_)
+                                nozzle_part_points[nozzle].pop(index_)
+                                break
+                        feeder_division_points[part] = 0
+                else:
+                    # 宸叉湁鐨勪緵鏂欏櫒
+                    feeder_base_points[best_assign_slot + idx * intv_ratio] -= min(
+                        filter(lambda x: x > 0, best_assign_points))
+
+                # 鏇存柊渚涙枡鍣ㄥ熀搴т俊鎭�
+                feeder_base[best_assign_slot + idx * intv_ratio] = part
+
+                feeder_type, extra_slot = self.part_data.loc[part].fdr, 0
+                extra_width = feeder_width[feeder_type][0] + feeder_width[feeder_type][1] - slot_intv
+                while extra_width > 0:
+                    extra_slot += 1
+                    if feeder_base[best_assign_slot + idx * intv_ratio + extra_slot] == -2:
+                        feeder_base[best_assign_slot + idx * intv_ratio + extra_slot] = -1      # 鏍囪妲戒綅宸插崰鐢�
+                    else:
+                        assert 'feeder allocation conflict'
+                    extra_width -= slot_intv
+
+                # 鏇存柊鍚稿槾淇℃伅
+                nozzle_pattern[idx] = self.part_data.loc[part].nz
+
+                # 鏇存柊澶村垎閰嶇殑鍏堝悗椤哄簭
+                head_assign_indexes = np.array(best_assign_points).argsort().tolist()
+
+            nozzle_part, nozzle_part_points = copy.deepcopy(best_nozzle_part), copy.deepcopy(
+                best_nozzle_part_points)
+
+            assert not list(filter(lambda x: x < 0, feeder_limit.values()))     # 鍒嗛厤渚涙枡鍣ㄦ暟鐩湪闄愬埗鑼冨洿鍐�
+
+        # 鏇存柊渚涙枡鍣ㄥ崰浣嶄俊鎭�
+        for _, data in feeder_data.iterrows():
+            feeder_base[data.slot] = -1
+
+        for slot, feeder in enumerate(feeder_base):
+            if feeder < 0:
+                continue
+            part = self.part_data.loc[feeder].part
+
+            feeder_data.loc[len(feeder_data.index)] = [slot, part]
+
+        if figure:
+            slotf1_pos = self.config.slotf1_pos
+            # 缁樺埗渚涙枡鍣ㄤ綅缃竷灞�
+            for slot in range(slot_num // 2):
+                plt.scatter(slotf1_pos.x + slot_intv * slot, slotf1_pos.y, marker='x', s=12, color='black', alpha=0.5)
+                plt.text(slotf1_pos.x + slot_intv * slot, slotf1_pos.y - 45, str(slot + 1), ha='center', va='bottom',
+                         size=8)
+
+            feeder_assign_range = []
+            for _, feeder in feeder_data.iterrows():
+                index = self.part_data[self.part_data.part == feeder.part].index.tolist()[0]
+                feeder_type = self.part_data.loc[index].fdr
+                width = feeder_width[feeder_type][0] + feeder_width[feeder_type][1]
+                start = slotf1_pos.x + slot_intv * (feeder.slot - 1) - slot_intv / 2
+                end = slotf1_pos.x + slot_intv * (feeder.slot - 1) - slot_intv / 2 + width
+
+                rec_x = [start, end, end, start]
+                rec_y = [slotf1_pos.y - 40, slotf1_pos.y - 40, slotf1_pos.y + 10, slotf1_pos.y + 10]
+
+                c = 'red' if feeder.arg == 0 else 'black'        # 榛戣壊琛ㄧず宸插垎閰嶏紝绾㈣壊琛ㄧず鏂板垎閰�
+                plt.text(slotf1_pos.x + slot_intv * (feeder.slot - 1), slotf1_pos.y + 12,
+                         feeder.part + ': ' + str(feeder_points[index]), ha='center', size=7, rotation=90, color=c)
+
+                plt.fill(rec_x, rec_y, facecolor='yellow', alpha=0.4)
+
+                feeder_assign_range.append([start, end])
+
+            # 璁板綍閲嶅彔鍖洪棿
+            feeder_assign_range.sort(key=lambda x: x[0])
+            for i in range(1, len(feeder_assign_range)):
+                if feeder_assign_range[i][0] < feeder_assign_range[i - 1][1]:
+                    start, end = feeder_assign_range[i][0], feeder_assign_range[i - 1][1]
+
+                    rec_x = [start, end, end, start]
+                    rec_y = [slotf1_pos.y - 40, slotf1_pos.y - 40, slotf1_pos.y + 10, slotf1_pos.y + 10]
+                    plt.fill(rec_x, rec_y, facecolor='red')
+
+            plt.plot([slotf1_pos.x - slot_intv / 2, slotf1_pos.x + slot_intv * (slot_num // 2 - 1 + 0.5)],
+                     [slotf1_pos.y + 10, slotf1_pos.y + 10], color='black')
+            plt.plot([slotf1_pos.x - slot_intv / 2, slotf1_pos.x + slot_intv * (slot_num // 2 - 1 + 0.5)],
+                     [slotf1_pos.y - 40, slotf1_pos.y - 40], color='black')
+
+            for counter in range(slot_num // 2 + 1):
+                pos = slotf1_pos.x + (counter - 0.5) * slot_intv
+                plt.plot([pos, pos], [slotf1_pos.y + 10, slotf1_pos.y - 40], color='black', linewidth=1)
+
+            plt.ylim(-10, 100)
+            plt.show()
+
+    def feeder_base_scan(self, feeder_data):
+        feeder_assign_check = set()
+        for _, feeder in feeder_data.iterrows():
+            feeder_assign_check.add(feeder.part)
+
+        part_index, part_points = defaultdict(int), defaultdict(int)
+        for idx, data in self.part_data.iterrows():
+            part_index[data.part] = idx
+        for _, data in self.step_data.iterrows():
+            part_points[part_index[data.part]] += 1
+
+        # assert len(feeder_assign_check) == len(part_points.values()) - list(part_points.values()).count(0)    # 鎵�鏈変緵鏂欏櫒鍧囧凡鍒嗛厤妲戒綅
+
+        mount_center_slot = defaultdict(float)
+        for _, data in self.step_data.iterrows():
+            idx = part_index[data.part]
+            mount_center_slot[idx] += (data.x - mount_center_slot[idx])
+
+        for idx, pos in mount_center_slot.items():
+            mount_center_slot[idx] = (pos / part_points[idx] + self.config.stopper_pos.x -
+                                      self.config.slotf1_pos.x) / self.config.slot_intv + 1
+
+        head_num, slot_num = self.config.head_num, self.config.slot_num
+        intv_ratio = round(self.config.head_intv / self.config.slot_intv)
+        feeder_part = [-1] * slot_num
+        for _, data in feeder_data.iterrows():
+            part_index = self.part_data[self.part_data.part == data.part].index.tolist()
+            if len(part_index) != 1:
+                print('unregistered component: ', data.part, ' in slot', data.slot)
+                continue
+            part_index = part_index[0]
+            feeder_part[data.slot] = part_index
+
+        part_result, cycle_result, slot_result = [], [], []  # 璐磋鐐圭储寮曞拰鎷惧彇妲戒綅浼樺寲缁撴灉
+
+        sum_nozzle_points, nozzle_pattern = -1, None
+        for slot in range(slot_num // 2 - (head_num - 1) * intv_ratio):
+            cur_nozzle_points, cur_nozzle_pattern = 0, ['' for _ in range(head_num)]
+            for head in range(head_num):
+                if (part := feeder_part[slot + head * intv_ratio]) == -1:
+                    continue
+                cur_nozzle_pattern[head] = self.part_data.loc[part].nz
+                cur_nozzle_points += part_points[part]
+            if cur_nozzle_points > sum_nozzle_points:
+                sum_nozzle_points = cur_nozzle_points
+                nozzle_pattern = cur_nozzle_pattern
+
+        nozzle_mode, nozzle_mode_cycle = [nozzle_pattern], [0]      # 鍚稿槾鍖归厤妯″紡
+
+        value_increment_base = 0
+        while True:
+            # === 鍛ㄦ湡鍐呭惊鐜� ===
+            assigned_part = [-1 for _ in range(head_num)]  # 褰撳墠鎵弿鍒扮殑澶村垎閰嶅厓浠朵俊鎭�
+            assigned_cycle = [0 for _ in range(head_num)]  # 褰撳墠鎵弿鍒扮殑鍏冧欢鏈�澶у垎閰嶆鏁�
+            assigned_slot = [-1 for _ in range(head_num)]  # 褰撳墠鎵弿鍒扮殑渚涙枡鍣ㄥ垎閰嶄俊鎭�
+
+            best_assigned_eval_func = -float('inf')
+            nozzle_insert_cycle = 0
+            for cycle_index, nozzle_cycle in enumerate(nozzle_mode):
+                scan_eval_func_list = []        # 鑻ュ共娆℃壂鎻忓緱鍒扮殑鏈�浼樿В
+                # nozzle_cycle 鍚稿槾妯″紡涓嬶紝宸叉壂鎻忓埌鐨勬渶浼樼粨鏋�
+                cur_scan_part = [-1 for _ in range(head_num)]
+                cur_scan_cycle = [0 for _ in range(head_num)]
+                cur_scan_slot = [-1 for _ in range(head_num)]
+                cur_nozzle_limit = copy.deepcopy(nozzle_limit)
+
+                while True:
+                    best_scan_part = [-1 for _ in range(head_num)]
+                    best_scan_cycle = [0 for _ in range(head_num)]
+                    best_scan_slot = [-1 for _ in range(head_num)]
+
+                    best_scan_nozzle_limit = copy.deepcopy(cur_nozzle_limit)
+                    scan_eval_func, search_break = -float('inf'), True
+
+                    # 鍓嶄緵鏂欏櫒鍩哄骇鎵弿
+                    for slot in range(1, slot_num // 2 - (head_num - 1) * intv_ratio + 1):
+                        if sum(feeder_part[slot: slot + head_num * intv_ratio: intv_ratio]) == -head_num:
+                            continue
+
+                        scan_cycle, scan_part, scan_slot = cur_scan_cycle.copy(), cur_scan_part.copy(), cur_scan_slot.copy()
+                        scan_nozzle_limit = copy.deepcopy(cur_nozzle_limit)
+
+                        # 棰勬壂鎻忕‘瀹氬悇绫诲瀷鍏冧欢鎷惧彇鏁扮洰锛堝墠鐬伙級
+                        preview_scan_part = defaultdict(int)
+                        for head in range(head_num):
+                            part = feeder_part[slot + head * intv_ratio]
+
+                            # 璐磋澶村拰鎷惧彇妲戒綅婊¤冻瀵瑰簲鍏崇郴
+                            if scan_part[head] == -1 and part != -1 and part_points[part] > 0 and scan_part.count(
+                                    part) < part_points[part]:
+                                preview_scan_part[part] += 1
+
+                        part_counter = 0
+                        for head in range(head_num):
+                            part = feeder_part[slot + head * intv_ratio]
+                            # 1.鍖归厤鏉′欢婊¤冻: 璐磋澶村拰鎷惧彇妲戒綅婊¤冻瀵瑰簲鍏崇郴
+                            if scan_part[head] == -1 and part != -1 and part_points[part] > 0 and scan_part.count(
+                                    part) < part_points[part]:
+                                # 2.鍖归厤鏉′欢婊¤冻锛氫笉瓒呰繃鍙敤鍚稿槾鏁扮殑闄愬埗
+                                nozzle = self.part_data.loc[part].nz
+                                if scan_nozzle_limit[nozzle] <= 0:
+                                    continue
+
+                                # 3.澧為噺鏉′欢婊¤冻: 寮曞叆鏂扮殑鍏冧欢绫诲瀷涓嶄細浣夸唬浠峰嚱鏁扮殑鍊煎噺灏�(鍓嶇灮)
+                                if scan_cycle.count(0) == head_num:
+                                    gang_pick_change = part_points[part]
+                                else:
+                                    prev_cycle = min(filter(lambda x: x > 0, scan_cycle))
+                                    # 鍚屾椂鎷惧彇鏁扮殑鎻愬崌
+                                    gang_pick_change = min(prev_cycle, part_points[part] // preview_scan_part[part])
+
+                                # 4.鎷惧彇绉诲姩璺濈鏉′欢婊¤冻: 閭昏繎鍏冧欢杩涜鍚屾椂鎶撳彇锛岄檷浣庣Щ鍔ㄨ矾寰勯暱搴�
+                                # reference_slot = -1
+                                # for head_, slot_ in enumerate(scan_slot):
+                                #     if slot_ != -1:
+                                #         reference_slot = slot_ - head_ * intv_ratio
+                                # if reference_slot != -1 and abs(reference_slot - slot) > (head_num - 1) * intv_ratio:
+                                #     continue
+
+                                # 5.鍚屾椂鎷惧彇鐨勫閲� 鍜� 鍚稿槾鏇存崲娆℃暟姣旇緝
+                                prev_nozzle_change = 0
+                                if cycle_index + 1 < len(nozzle_mode):
+                                    prev_nozzle_change = 2 * (nozzle_cycle[head] != nozzle_mode[cycle_index + 1][head])
+
+                                # 閬垮厤棣栦釜鍛ㄦ湡鍚告潌鍗犵敤鐜囦綆鐨勯棶棰�
+                                nozzle_change = 2 * (nozzle != nozzle_cycle[head])
+
+                                if cycle_index + 1 < len(nozzle_mode):
+                                    nozzle_change += 2 * (nozzle != nozzle_mode[cycle_index + 1][head])
+                                nozzle_change -= prev_nozzle_change
+
+                                val = self.e_gang_pick * gang_pick_change - self.e_nz_change * nozzle_change
+                                if val < value_increment_base:
+                                    continue
+                                part_counter += 1
+
+                                scan_part[head] = part
+                                scan_cycle[head] = part_points[part] // preview_scan_part[part]
+                                scan_slot[head] = slot + head * intv_ratio
+
+                                scan_nozzle_limit[nozzle] -= 1
+
+                        nozzle_counter = 0          # 鍚稿槾鏇存崲娆℃暟
+                        # 涓婁竴鍛ㄦ湡
+                        for head, nozzle in enumerate(nozzle_cycle):
+                            if scan_part[head] == -1:
+                                continue
+                            if self.part_data.loc[scan_part[head]].nz != nozzle and nozzle != '':
+                                nozzle_counter += 2
+
+                        # 涓嬩竴鍛ㄦ湡锛堥澶栧鍔犵殑鍚稿槾鏇存崲娆℃暟锛�
+                        if cycle_index + 1 < len(nozzle_mode):
+                            for head, nozzle in enumerate(nozzle_mode[cycle_index + 1]):
+                                if scan_part[head] == -1:
+                                    continue
+                                prev_counter, new_counter = 0, 0
+                                if nozzle_cycle[head] != nozzle and nozzle_cycle[head] != '' and nozzle != '':
+                                    prev_counter += 2
+                                if self.part_data.loc[scan_part[head]].nz != nozzle and nozzle != '':
+                                    new_counter += 2
+                                nozzle_counter += new_counter - prev_counter
+                        else:
+                            for head, nozzle in enumerate(nozzle_mode[0]):
+                                if scan_part[head] == -1:
+                                    continue
+                                prev_counter, new_counter = 0, 0
+                                if nozzle_cycle[head] != nozzle and nozzle_cycle[head] != '' and nozzle != '':
+                                    prev_counter += 2
+                                if self.part_data.loc[scan_part[head]].nz != nozzle and nozzle != '':
+                                    new_counter += 2
+                                nozzle_counter += new_counter - prev_counter
+
+                        if part_counter == 0:      # 褰撳墠鎯呭舰涓嬫湭鎵弿鍒颁换浣曞厓浠�
+                            continue
+                        search_break = False
+
+                        scan_part_head = defaultdict(list)
+                        for head, part in enumerate(scan_part):
+                            if part == -1:
+                                continue
+                            scan_part_head[part].append(head)
+
+                        for part, heads in scan_part_head.items():
+                            part_cycle = part_points[part] // len(heads)
+                            for head in heads:
+                                scan_cycle[head] = part_cycle
+
+                        # 璁＄畻鎵弿鍚庣殑浠ｄ环鍑芥暟,璁板綍鎵弿鍚庣殑鏈�浼樿В
+                        # 鐭湡鏀剁泭
+                        cycle = min(filter(lambda x: x > 0, scan_cycle))
+                        gang_pick_counter, gang_pick_slot_set = 0, set()
+                        for head, pick_slot in enumerate(scan_slot):
+                            gang_pick_slot_set.add(pick_slot - head * intv_ratio)
+
+                        eval_func_short_term = self.e_gang_pick * (head_num - scan_slot.count(-1) - len(
+                            gang_pick_slot_set)) * cycle - self.e_nz_change * nozzle_counter
+
+                        # 闀挎湡鏀剁泭
+                        gang_pick_slot_dict = defaultdict(list)
+                        for head, pick_slot in enumerate(scan_slot):
+                            gang_pick_slot_dict[pick_slot - head * intv_ratio].append(scan_cycle[head])
+
+                        eval_func_long_term = 0
+                        for pick_cycle in gang_pick_slot_dict.values():
+                            while pick_cycle:
+                                min_cycle = min(pick_cycle)
+                                eval_func_long_term += self.e_gang_pick * (len(pick_cycle) - 1) * min(pick_cycle)
+                                pick_cycle = list(map(lambda c: c - min_cycle, pick_cycle))
+                                pick_cycle = list(filter(lambda c: c > 0, pick_cycle))
+                        eval_func_long_term -= self.e_nz_change * nozzle_counter
+
+                        # 鎷惧彇杩囩▼涓殑绉诲姩璺緞
+                        pick_slot_set = set()
+                        for head, pick_slot in enumerate(scan_slot):
+                            if pick_slot == -1:
+                                continue
+                            pick_slot_set.add(pick_slot - head * intv_ratio)
+
+                        slot_offset = 0
+                        for head, part in enumerate(scan_part):
+                            if part == -1:
+                                continue
+                            slot_offset += abs(scan_slot[head] - mount_center_slot[part])
+
+                        ratio = 0.5
+                        eval_func = (1 - ratio) * eval_func_short_term + ratio * eval_func_long_term - 1e-5 * (
+                                    max(pick_slot_set) - min(pick_slot_set)) - 1e-5 * slot_offset
+                        if eval_func >= scan_eval_func:
+                            scan_eval_func = eval_func
+                            best_scan_part, best_scan_cycle = scan_part.copy(), scan_cycle.copy()
+                            best_scan_slot = scan_slot.copy()
+
+                            best_scan_nozzle_limit = copy.deepcopy(scan_nozzle_limit)
+
+                    if search_break:
+                        break
+                    scan_eval_func_list.append(scan_eval_func)
+
+                    cur_scan_part = best_scan_part.copy()
+                    cur_scan_slot = best_scan_slot.copy()
+                    cur_scan_cycle = best_scan_cycle.copy()
+
+                    cur_nozzle_limit = copy.deepcopy(best_scan_nozzle_limit)
+
+                if len(scan_eval_func_list) and sum(scan_eval_func_list) > best_assigned_eval_func:
+                    best_assigned_eval_func = sum(scan_eval_func_list)
+
+                    assigned_part = cur_scan_part.copy()
+                    assigned_slot = cur_scan_slot.copy()
+                    assigned_cycle = cur_scan_cycle.copy()
+
+                    nozzle_insert_cycle = cycle_index
+
+            # 浠庝緵鏂欏櫒鍩哄骇涓Щ闄ゅ搴旀暟閲忕殑璐磋鐐�
+            nonzero_cycle = [cycle for cycle in assigned_cycle if cycle > 0]
+            if not nonzero_cycle:
+                value_increment_base -= head_num
+                continue
+
+            for head, slot in enumerate(assigned_slot):
+                if assigned_part[head] == -1:
+                    continue
+                part_points[feeder_part[slot]] -= min(nonzero_cycle)
+
+            insert_cycle = sum([nozzle_mode_cycle[c] for c in range(nozzle_insert_cycle + 1)])
+
+            part_result.insert(insert_cycle, assigned_part)
+            cycle_result.insert(insert_cycle, min(nonzero_cycle))
+            slot_result.insert(insert_cycle, assigned_slot)
+
+            # 鏇存柊鍚稿槾鍖归厤妯″紡
+            cycle_nozzle = nozzle_mode[nozzle_insert_cycle].copy()
+            for head, part in enumerate(assigned_part):
+                if part == -1:
+                    continue
+                cycle_nozzle[head] = self.part_data.loc[part].nz
+
+            if cycle_nozzle == nozzle_mode[nozzle_insert_cycle]:
+                nozzle_mode_cycle[nozzle_insert_cycle] += 1
+            elif nozzle_insert_cycle + 1 < len(nozzle_mode) and cycle_nozzle == nozzle_mode[nozzle_insert_cycle + 1]:
+                nozzle_mode_cycle[nozzle_insert_cycle + 1] += 1
+            else:
+                nozzle_mode.insert(nozzle_insert_cycle + 1, cycle_nozzle)
+                nozzle_mode_cycle.insert(nozzle_insert_cycle + 1, 1)
+
+            if sum(part_points.values()) == 0:
+                break
+
+        return part_result, cycle_result, slot_result
diff --git a/opt/smm/hybrid_genetic.py b/opt/smm/hybrid_genetic.py
new file mode 100644
index 0000000..1a37fc4
--- /dev/null
+++ b/opt/smm/hybrid_genetic.py
@@ -0,0 +1,535 @@
+from opt.smm.basis import *
+from opt.utils import *
+
+
+class HybridGeneticOpt(BaseOpt):
+    def __init__(self, config, part_data, step_data, feeder_data=None):
+        super().__init__(config, part_data, step_data, feeder_data)
+
+        self.part_nozzle = defaultdict(str)
+        self.part_point_pos = defaultdict(list)
+
+        self.designated_nozzle = [''] * self.config.head_num
+        self.designated_slot = [None] * self.config.slot_num
+
+        self.pickup_group = []        # pair_group:  pickups from same initial group
+        self.pickup_group_cycle = []
+        self.pair_group = []
+        self.feeder_part_arrange = defaultdict(list)
+
+    def pickup_group_combine(self, supply, supply_cycle, demand, demand_cycle):
+
+        combination, combination_cycle = demand.copy(), demand_cycle.copy()
+        supply_cpy = supply.copy()
+
+        while True:
+            supply_cpy_bits = self.config.head_num - supply_cpy.count(None)
+            if supply_cpy_bits == 0:
+                break
+            max_match_offset,  max_match_counter = 0, 0
+            supply_cpy_index = [idx for idx, part in enumerate(supply_cpy) if part]     # 鍔犲揩鎼滅储閫熷害
+            for offset in range(-supply_cpy_index[-1], self.config.head_num - supply_cpy_index[0]):
+                match_counter = 0
+                for idx, part in enumerate(supply_cpy):
+                    if 0 <= idx + offset < self.config.head_num:
+                        if part is None:
+                            continue
+                        if combination[idx + offset] is None and self.designated_nozzle[idx + offset] == self.designated_nozzle[idx]:
+                            match_counter += 1
+                if match_counter > max_match_counter:
+                    max_match_counter = match_counter
+                    max_match_offset = offset
+                    if match_counter == supply_cpy_bits:
+                        break
+
+            for idx, part in enumerate(supply_cpy):
+                if 0 <= idx + max_match_offset < self.config.head_num:
+                    if part is None:
+                        continue
+
+                    if demand[idx + max_match_offset] is None:
+                        combination[idx + max_match_offset] = part
+                        combination_cycle[idx + max_match_offset] = supply_cycle[idx]
+                        supply_cpy[idx] = None
+
+            if max_match_counter == 0:
+                break
+
+        return combination, combination_cycle
+
+    def cal_ind_val(self, individual):
+
+        place_time, pick_time = 0.234, 0.4
+        x_moving_speed, y_moving_speed = 300, 300  # mm/s
+
+        prev_pair_index = None
+        sequenced_pickup_group, sequenced_pickup_cycle = [], []
+        for gene in individual:
+            pickup = self.pickup_group[gene]
+
+            pair_index = None
+            for idx, pair in enumerate(self.pair_group):
+                if gene in pair:
+                    pair_index = idx
+                    break
+
+            if pair_index is not None and pair_index == prev_pair_index:
+                for idx, component in enumerate(pickup):
+                    sequenced_pickup_group[-1][idx] = component
+            else:
+                sequenced_pickup_group.append(pickup.copy())
+                sequenced_pickup_cycle.append(self.pickup_group_cycle[gene])
+
+        V = [float('inf') for _ in range(len(sequenced_pickup_group) + 1)]      # Node Value
+        V[0] = 0
+        V_SNode = [-1 for _ in range(len(sequenced_pickup_group) + 1)]
+
+        nozzle_assigned_heads = defaultdict(int)
+        for nozzle in self.designated_nozzle:
+            nozzle_assigned_heads[nozzle] += 1
+
+        pickup_result, pickup_cycle_result = [[] for _ in range(len(V))], [[] for _ in range(len(V))]
+        component_point_index = defaultdict(int)
+
+        intv_ratio = self.config.head_intv // self.config.head_intv
+        for i in range(1, len(V)):
+            cost, t0 = 0, 0
+            load = defaultdict(int)
+            Pd, Pd_cycle = [None for _ in range(self.config.head_num)], [0 for _ in range(self.config.head_num)]  # demand pickup
+            j = i
+            while j < len(V):
+                Ps, Ps_cycle = sequenced_pickup_group[j - 1], [sequenced_pickup_cycle[j - 1] for _ in
+                                                           range(self.config.head_num)]  # supply pickup and its cycle
+                for part in Ps:
+                    if part:
+                        load[self.part_nozzle[part]] += 1
+
+                is_combinable = True
+                for nozzle, counter in load.items():
+                    if counter > nozzle_assigned_heads[nozzle]:
+                        is_combinable = False
+
+                if is_combinable:
+                    cost = cost - t0
+                    # combine sequenced pickup 蟻b and ps into 蟻u(union pickup)
+                    Pu, Pu_cycle = self.pickup_group_combine(Ps, Ps_cycle, Pd, Pd_cycle)
+
+                    # decide the placement cluster and sequencing of pickup 蟻u
+                    pickup_action_counter, place_action_counter = 0, self.config.head_num - Pu.count(None)
+                    right_most_slot, left_most_slot = 0, self.config.slot_num // 2        # most left and right pickup slot
+
+                    # === TODO: 鏈烘闄愪綅銆佸悗妲戒綅鍒嗛厤鏈鐞� ===
+                    for head in range(self.config.head_num):
+                        if not Pu[head]:
+                            continue
+                        assert Pu[head] in self.feeder_part_arrange.keys()
+                        for slot in self.feeder_part_arrange[Pu[head]]:
+                            left_most_slot = min(slot - head * intv_ratio, left_most_slot)
+                            right_most_slot = max(slot - head * intv_ratio, right_most_slot)
+
+                    # calculate forward, backward, pick and place traveling time
+                    t_FW, t_BW, t_PL, t_PU = 0, 0, 0, 0
+                    cycle = 0
+                    while cycle < max(Pu_cycle):
+                        mount_points = []
+                        for head, part in enumerate(Pu):
+                            if part is None or cycle > Pu_cycle[head]:
+                                continue
+                            idx = component_point_index[part]
+                            mount_points.append(Point(self.part_point_pos[part][idx].x - head * self.config.head_intv
+                                                      + self.config.stopper_pos.x,
+                                                      self.part_point_pos[part][idx].y + self.config.stopper_pos.y))
+                        assert len(mount_points) > 0
+
+                        # calculate cycle moving distance
+                        mount_points.sort(key=lambda p: p.x)
+                        slotf1_pos = self.config.slotf1_pos
+                        t_FW += max(
+                            abs(slotf1_pos.x + (left_most_slot - 1) * self.config.slot_intv - mount_points[0].x) / x_moving_speed,
+                            abs(slotf1_pos.y - mount_points[0].y) / y_moving_speed)
+                        t_BW += max(
+                            abs(slotf1_pos.x + (right_most_slot - 1) * self.config.slot_intv - mount_points[-1].x) / x_moving_speed,
+                            abs(slotf1_pos.y - mount_points[-1].y) / y_moving_speed)
+                        # pick up moving time
+                        t_PU += (right_most_slot - left_most_slot) * self.config.slot_intv / x_moving_speed
+                        # place moving time
+                        for idx_points in range(len(mount_points) - 1):
+                            t_PL += max(abs(mount_points[idx_points].x - mount_points[idx_points + 1].x) / x_moving_speed,
+                                        abs(mount_points[idx_points].y - mount_points[idx_points + 1].y) / y_moving_speed)
+                        cycle += 1
+
+                    t0 = t_FW + (t_PL + place_action_counter * place_time) + t_BW
+                    cost += (t_PU + pickup_action_counter * pick_time) + t0
+
+                    if V[i - 1] + cost < V[j]:
+                        pickup_result[j], pickup_cycle_result[j] = Pu, Pu_cycle
+                        V_SNode[j] = i - 1
+                        V[j] = V[i - 1] + cost
+
+                    Pd, Pd_cycle = Pu, Pu_cycle
+                    j += 1
+                else:
+                    break
+
+        node = len(V) - 1
+        while True:
+            prev_node = V_SNode[node]
+            if prev_node == -1:
+                break
+            for k in range(prev_node + 1, node):
+                pickup_result[k], pickup_cycle_result[k] = [], []
+            node = prev_node
+        return V[-1], pickup_result, pickup_cycle_result
+
+    def convertor(self, individual):
+
+        part_result, cycle_result, feeder_slot_result = [], [], []
+        # initial result
+        _, pickup_result, pickup_cycle_result = self.cal_ind_val(individual)
+
+        for idx, pickup in enumerate(pickup_result):
+            while pickup and max(pickup_cycle_result[idx]) != 0:
+                cycle = min([cycle_ for cycle_ in pickup_cycle_result[idx] if cycle_ > 0])
+                feeder_part_arrange_index = defaultdict(int)
+                part_result.append([-1 for _ in range(self.config.head_num)])
+                feeder_slot_result.append([-1 for _ in range(self.config.head_num)])
+                cycle_result.append(cycle)
+                for head, part in enumerate(pickup):
+                    if part is None or pickup_cycle_result[idx][head] == 0:
+                        continue
+
+                    part_result[-1][head] = self.part_data[self.part_data['part'] == part].index.tolist()[0]
+                    feeder_slot_result[-1][head] = self.feeder_part_arrange[part][feeder_part_arrange_index[part]]
+                    feeder_part_arrange_index[part] += 1
+                    if feeder_part_arrange_index[part] >= len(self.feeder_part_arrange[part]):
+                        feeder_part_arrange_index[part] = 0
+
+                    pickup_cycle_result[idx][head] -= cycle
+
+        return part_result, cycle_result, feeder_slot_result
+
+    def optimal_nozzle_assignment(self):
+        if len(self.step_data) == 0:
+            return defaultdict(int)
+
+        # === Nozzle Assignment ===
+        # number of points for nozzle & number of heads for nozzle
+        nozzle_points, nozzle_assigned_counter = defaultdict(int), defaultdict(int)
+
+        for _, data in self.step_data.iterrows():
+            idx = self.part_data[self.part_data['part'] == data['part']].index.tolist()[0]
+            nozzle = self.part_data.loc[idx]['nz']
+
+            nozzle_assigned_counter[nozzle] = 0
+            nozzle_points[nozzle] += 1
+
+        assert len(nozzle_points.keys()) <= self.config.head_num
+        total_points, available_head = len(self.step_data), self.config.head_num
+        # S1: set of nozzle types which are sufficient to assign one nozzle to the heads
+        # S2: temporary nozzle set
+        # S3: set of nozzle types which already have the maximum reasonable nozzle amounts.
+        S1, S2, S3 = [], [], []
+
+        for nozzle in nozzle_points.keys():  # Phase 1
+            if nozzle_points[nozzle] * self.config.head_num < total_points:
+                nozzle_assigned_counter[nozzle] = 1
+                available_head -= 1
+                total_points -= nozzle_points[nozzle]
+
+                S1.append(nozzle)
+            else:
+                S2.append(nozzle)
+
+        available_head_ = available_head  # Phase 2
+        for nozzle in S2:
+            nozzle_assigned_counter[nozzle] = math.floor(available_head * nozzle_points[nozzle] / total_points)
+            available_head_ = available_head_ - nozzle_assigned_counter[nozzle]
+
+        S2.sort(key=lambda x: nozzle_points[x] / (nozzle_assigned_counter[x] + 1e-10), reverse=True)
+        while available_head_ > 0:
+            nozzle = S2[0]
+            nozzle_assigned_counter[nozzle] += 1
+
+            S2.remove(nozzle)
+            S3.append(nozzle)
+            available_head_ -= 1
+
+        phase_iteration = len(S2) - 1
+        while phase_iteration > 0:  # Phase 3
+            nozzle_i_val, nozzle_j_val = 0, 0
+            nozzle_i, nozzle_j = None, None
+            for nozzle in S2:
+                if nozzle_i is None or nozzle_points[nozzle] / nozzle_assigned_counter[nozzle] > nozzle_i_val:
+                    nozzle_i_val = nozzle_points[nozzle] / nozzle_assigned_counter[nozzle]
+                    nozzle_i = nozzle
+
+                if nozzle_assigned_counter[nozzle] > 1:
+                    if nozzle_j is None or nozzle_points[nozzle] / (nozzle_assigned_counter[nozzle] - 1) < nozzle_j_val:
+                        nozzle_j_val = nozzle_points[nozzle] / (nozzle_assigned_counter[nozzle] - 1)
+                        nozzle_j = nozzle
+
+            if nozzle_i and nozzle_j and nozzle_points[nozzle_j] / (nozzle_assigned_counter[nozzle_j] - 1) < \
+                    nozzle_points[nozzle_i] / nozzle_assigned_counter[nozzle_i]:
+                nozzle_assigned_counter[nozzle_j] -= 1
+                nozzle_assigned_counter[nozzle_i] += 1
+                S2.remove(nozzle_i)
+                S3.append(nozzle_i)
+            else:
+                break
+
+        return nozzle_assigned_counter
+
+    @timer_wrapper
+    def optimize(self):
+        nozzle_assigned_counter = self.optimal_nozzle_assignment()
+
+        # nozzle assignment result:
+        self.designated_nozzle = [''] * self.config.head_num
+        head_index = 0
+        for nozzle, num in nozzle_assigned_counter.items():
+            while num > 0:
+                self.designated_nozzle[head_index] = nozzle
+                head_index += 1
+                num -= 1
+
+        # === component assignment ===
+        part_points, nozzle_components = defaultdict(int), defaultdict(list)   # 鍏冧欢璐磋鐐规暟锛屽惛鍢�-鍏冧欢瀵瑰簲鍏崇郴
+        component_feeder_limit, component_divided_points = defaultdict(int), defaultdict(list)
+        for _, data in self.step_data.iterrows():
+            part = data['part']
+            idx = self.part_data[self.part_data['part'] == part].index.tolist()[0]
+            nozzle = self.part_data.loc[idx]['nz']
+
+            component_feeder_limit[part] = self.part_data.loc[idx].fdn
+            part_points[part] += 1
+            if nozzle_components[nozzle].count(part) < component_feeder_limit[part]:
+                nozzle_components[nozzle].append(part)
+
+        for part, feeder_limit in component_feeder_limit.items():
+            for _ in range(feeder_limit):
+                component_divided_points[part].append(part_points[part] // feeder_limit)
+
+        for part, divided_points in component_divided_points.items():
+            index = 0
+            while sum(divided_points) < part_points[part]:
+                divided_points[index] += 1
+                index += 1
+
+        CT_Group, CT_Points = [], []    # CT: Component Type
+        while sum(len(nozzle_components[nozzle]) for nozzle in nozzle_components.keys()) != 0:
+
+            CT_Group.append([None for _ in range(self.config.head_num)])
+            CT_Points.append([0 for _ in range(self.config.head_num)])
+
+            for head_index in range(self.config.head_num):
+                nozzle = self.designated_nozzle[head_index]      # 鍒嗛厤鐨勫惛鍢�
+                if len(nozzle_components[nozzle]) == 0:      # 鏃犲彲鐢ㄥ厓浠�
+                    continue
+
+                max_points, designated_part = 0, None
+                for part in nozzle_components[nozzle]:
+                    if part_points[part] > max_points:
+                        max_points = part_points[part]
+                        designated_part = part
+
+                part_points[designated_part] -= component_divided_points[designated_part][-1]
+
+                CT_Group[-1][head_index] = designated_part
+                CT_Points[-1][head_index] = component_divided_points[designated_part][-1]
+
+                component_divided_points[designated_part].pop()
+                nozzle_components[nozzle].remove(designated_part)
+
+        # === assign CT group to feeder slot ===
+        for _, data in self.step_data.iterrows():
+            self.part_point_pos[data.part].append(Point(data.x + self.config.stopper_pos.x,
+                                                        data.y + self.config.stopper_pos.y))
+
+        for pos_list in self.part_point_pos.values():
+            pos_list.sort(key=lambda p: (p.x, p.y))
+
+        CT_Group_slot = [-1] * len(CT_Group)
+        feeder_lane = [None] * self.config.slot_num     # 渚涙枡鍣ㄥ熀搴т笂宸插垎閰嶇殑鍏冧欢绫诲瀷
+        CT_Head = defaultdict(list)
+        for pickup in CT_Group:
+            for head, CT in enumerate(pickup):
+                if CT is None:
+                    continue
+                if CT not in CT_Head:
+                    CT_Head[CT] = [head, head]
+                CT_Head[CT][0] = min(CT_Head[CT][0], head)
+                CT_Head[CT][1] = max(CT_Head[CT][1], head)
+
+        intv_ratio = self.config.head_intv // self.config.slot_intv
+        for CTIdx, pickup in enumerate(CT_Group):
+            best_slot = []
+            for cp_index, part in enumerate(pickup):
+                if part is None:
+                    continue
+                best_slot.append(round((sum(p.x for p in self.part_point_pos[part]) / len(
+                    self.part_point_pos[part]) - self.config.slotf1_pos.x) / self.config.slot_intv) + 1 - cp_index * intv_ratio)
+            best_slot = round(sum(best_slot) / len(best_slot))
+
+            search_dir, step = 0, 0  # dir: 1-鍚戝彸, 0-鍚戝乏
+            prev_assign_available = True
+            while True:
+                assign_slot = best_slot + step if search_dir else best_slot - step
+                if assign_slot + (len(pickup) - 1) * intv_ratio >= self.config.slot_num / 2 or assign_slot < 0:
+                    if not prev_assign_available:
+                        raise Exception('feeder assign error!')
+
+                    # prev_assign_available = False
+                    search_dir = 1 - search_dir
+                    if search_dir == 1:
+                        step += 1
+                    continue
+
+                prev_assign_available = True
+                assign_available = True
+
+                # 鍒嗛厤瀵瑰簲妲戒綅
+                for slot in range(assign_slot, assign_slot + intv_ratio * len(pickup), intv_ratio):
+                    pickup_index = int((slot - assign_slot) / intv_ratio)
+                    pick_part = pickup[pickup_index]
+
+                    # 妫�鏌ユЫ浣嶅崰鐢ㄦ儏鍐�
+                    if feeder_lane[slot] and pick_part:
+                        assign_available = False
+                        break
+
+                    # 妫�鏌ユ満姊伴檺浣嶅啿绐�
+                    if pick_part and (slot - CT_Head[pick_part][0] * intv_ratio <= 0 or slot + (
+                            self.config.head_num - CT_Head[pick_part][1] - 1) * intv_ratio > self.config.slot_num // 2):
+                        assign_available = False
+                        break
+
+                if assign_available:
+                    for idx, part in enumerate(pickup):
+                        if part:
+                            feeder_lane[assign_slot + idx * intv_ratio] = part
+                    CT_Group_slot[CTIdx] = assign_slot
+                    break
+
+                search_dir = 1 - search_dir
+                if search_dir == 1:
+                    step += 1
+
+        # === Initial Pickup Group ===
+        initial_pickup, initial_pickup_cycle = [], []
+        for index, CT in enumerate(CT_Group):
+            while True:
+                if CT_Points[index].count(0) == self.config.head_num:
+                    break
+                min_element = min([Points for Points in CT_Points[index] if Points > 0])
+
+                initial_pickup.append(copy.deepcopy(CT_Group[index]))
+                initial_pickup_cycle.append(min_element)
+                for head in range(self.config.head_num):
+                    if CT_Points[index][head] >= min_element:
+                        CT_Points[index][head] -= min_element
+                    if CT_Points[index][head] == 0:
+                        CT_Group[index][head] = None
+
+        # pickup partition rule
+        partition_probability = 0.1
+        for idx, Pickup in enumerate(initial_pickup):
+            pickup_num = len([element for element in Pickup if element is not None])
+            if 2 <= pickup_num <= self.config.head_num / 3 or (
+                    self.config.head_num / 3 <= pickup_num <= self.config.head_num / 2 and np.random.rand() < partition_probability):
+                # partitioned into single component pickups
+                # or partition the potentially inefficient initial pickups with a small probability
+                pair_index = []
+                for index, CT in enumerate(Pickup):
+                    if CT is not None:
+                        pair_index.append(len(self.pickup_group))
+                        self.pickup_group.append([None for _ in range(self.config.head_num)])
+                        self.pickup_group[-1][index] = CT
+                        self.pickup_group_cycle.append(initial_pickup_cycle[idx])
+                self.pair_group.append(pair_index)
+            else:
+                self.pickup_group.append(Pickup)
+                self.pickup_group_cycle.append(initial_pickup_cycle[idx])
+
+        # basic parameter
+        # crossover rate & mutation rate: 80% & 10%
+        # population size: 200
+        # the number of generation: 500
+        crossover_rate, mutation_rate = 0.8, 0.1
+        population_size, n_generations = 200, 500
+
+        # initial solution
+        population = []
+        for _ in range(population_size):
+            pop_permutation = list(range(len(self.pickup_group)))
+            np.random.shuffle(pop_permutation)
+            population.append(pop_permutation)
+
+        best_individual, best_pop_val = [], []
+
+        # === 璁板綍涓嶅悓鍏冧欢瀵瑰簲鐨勬Ы浣� ===
+        self.feeder_part_arrange = defaultdict(list)
+        for slot in range(1, self.config.slot_num // 2 + 1):
+            if feeder_lane[slot]:
+                self.feeder_part_arrange[feeder_lane[slot]].append(slot)
+
+        # === 璁板綍涓嶅悓鍏冧欢鐨勬敞鍐屽惛鍢寸被鍨� ===
+        self.part_nozzle = defaultdict(str)
+        for pickup in self.pickup_group:
+            for part in pickup:
+                if part is None or part in self.part_nozzle.keys():
+                    continue
+                self.part_nozzle[part] = self.part_data[self.part_data['part'] == part]['nz'].tolist()[0]
+
+        with tqdm(total=n_generations) as pbar:
+            pbar.set_description('hybrid genetic process')
+            # calculate fitness value
+            pop_val = [self.cal_ind_val(individual)[0] for individual in population]  # val is related to assembly time
+
+            for _ in range(n_generations):
+                # min-max convert
+                max_val = 1.5 * max(pop_val)
+                convert_pop_val = list(map(lambda v: max_val - v, pop_val))
+
+                # crossover and mutation
+                c = 0
+                new_population, new_pop_val = [], []
+                for pop in range(population_size):
+                    if pop % 2 == 0 and np.random.random() < crossover_rate:
+                        index1, index2 = GenOpe.roulette_wheel_selection(convert_pop_val), -1
+                        while True:
+                            index2 = GenOpe.roulette_wheel_selection(convert_pop_val)
+                            if index1 != index2:
+                                break
+                        # 涓ょ偣浜ゅ弶绠楀瓙
+                        offspring1 = GenOpe.directed_edge_recombine_crossover(population[index1], population[index2])
+                        offspring2 = GenOpe.directed_edge_recombine_crossover(population[index2], population[index1])
+
+                        if np.random.random() < mutation_rate:
+                            GenOpe.swap_mutation(offspring1)
+
+                        if np.random.random() < mutation_rate:
+                            GenOpe.swap_mutation(offspring2)
+
+                        new_population.append(offspring1)
+                        new_population.append(offspring2)
+
+                        new_pop_val.append(self.cal_ind_val(offspring1)[0])
+                        new_pop_val.append(self.cal_ind_val(offspring2)[0])
+
+                # generate next generation
+                top_k_index = GenOpe.get_top_kth(pop_val, population_size - len(new_population), reverse=False)
+                for index in top_k_index:
+                    new_population.append(population[index])
+                    new_pop_val.append(pop_val[index])
+
+                population = new_population
+                pop_val = new_pop_val
+                pbar.update(1)
+
+        best_individual = population[np.argmin(pop_val)]
+        self.result.part, self.result.cycle, self.result.slot = self.convertor(best_individual)
+        self.result.point, self.result.sequence = self.path_planner.greedy_cluster(self.result.part, self.result.cycle,
+                                                                                   self.result.slot)
+
+
+
diff --git a/opt/smm/path_plan.py b/opt/smm/path_plan.py
new file mode 100644
index 0000000..1ea8df4
--- /dev/null
+++ b/opt/smm/path_plan.py
@@ -0,0 +1,290 @@
+import copy
+
+import numpy as np
+
+from opt.utils import axis_moving_time
+from core.common import *
+from data.type import Point
+
+
+class PathPlanOpt:
+    def __init__(self, config, part_data, step_data):
+        self.part_data = part_data
+        self.step_data = step_data
+        self.config = config
+
+    def dynamic_programming_cycle_path(self, cycle_point, cycle_slot):
+        head_sequence = []
+        num_pos = sum([placement != -1 for placement in cycle_point]) + 1
+        intv_ratio = self.config.head_intv // self.config.slot_intv
+
+        pos, head_set = [], []
+        feeder_set = set()
+        for head, slot in enumerate(cycle_slot):
+            if slot == -1:
+                continue
+
+            head_set.append(head)
+            placement = cycle_point[head]
+            pos.append([self.step_data.loc[placement]['x'] - head * self.config.head_intv + self.config.stopper_pos.x,
+                        self.step_data.loc[placement]['y'] + self.config.stopper_pos.y,
+                        self.step_data.loc[placement]['r'], head])
+
+            feeder_set.add(slot - head * intv_ratio)
+
+        pos.insert(0, [self.config.slotf1_pos.x + ((min(list(feeder_set)) + max(list(feeder_set))) / 2 - 1) *
+                       self.config.slot_intv, self.config.slotf1_pos.y, None, 0])
+
+        def get_distance(pos_1, pos_2):
+            # 拾取起始与终止位置 或 非同轴
+            if pos_1[2] is None or pos_2[2] is None or pos_1[3] + (1 if pos_1[3] % 2 == 0 else -1) != pos_2[3]:
+                return max(axis_moving_time(pos_1[0] - pos_2[0], 0), axis_moving_time(pos_1[1] - pos_2[1], 1))
+            else:
+                return max(axis_moving_time(pos_1[0] - pos_2[0], 0), axis_moving_time(pos_1[1] - pos_2[1], 1),
+                           axis_moving_time(pos_1[2] - pos_2[2], 2))
+
+        # 各节点之间的距离
+        dist = [[get_distance(pos_1, pos_2) for pos_2 in pos] for pos_1 in pos]
+
+        min_dist = [[np.inf for _ in range(num_pos)] for s in range(1 << num_pos)]
+        min_path = [[[] for _ in range(num_pos)] for s in range(1 << num_pos)]
+
+        # 状压dp搜索
+        for s in range(1, 1 << num_pos, 2):
+            # 考虑节点集合s必须包括节点0
+            if not (s & 1):
+                continue
+            for j in range(1, num_pos):
+                # 终点j需在当前考虑节点集合s内
+                if not (s & (1 << j)):
+                    continue
+                if s == int((1 << j) | 1):
+                    # 若考虑节点集合s仅含节点0和节点j，dp边界，赋予初值
+                    # print('j:', j)
+                    min_path[s][j] = [j]
+                    min_dist[s][j] = dist[0][j]
+
+                # 枚举下一个节点i，更新
+                for i in range(1, num_pos):
+                    # 下一个节点i需在考虑节点集合s外
+                    if s & (1 << i):
+                        continue
+                    if min_dist[s][j] + dist[j][i] < min_dist[s | (1 << i)][i]:
+                        min_path[s | (1 << i)][i] = min_path[s][j] + [i]
+                        min_dist[s | (1 << i)][i] = min_dist[s][j] + dist[j][i]
+
+        ans_dist = float('inf')
+        ans_path = []
+        # 求最终最短哈密顿回路
+        for i in range(1, num_pos):
+            if min_dist[(1 << num_pos) - 1][i] + dist[i][0] < ans_dist:
+                # 更新，回路化
+                ans_path = min_path[s][i]
+                ans_dist = min_dist[(1 << num_pos) - 1][i] + dist[i][0]
+
+        for parent in ans_path:
+            head_sequence.append(head_set[parent - 1])
+
+        start_head, end_head = head_sequence[0], head_sequence[-1]
+        if self.step_data.loc[cycle_point[start_head]]['x'] - start_head * self.config.head_intv > \
+                self.step_data.loc[cycle_point[end_head]]['x'] - end_head * self.config.head_intv:
+            head_sequence = list(reversed(head_sequence))
+        return ans_dist, head_sequence
+
+    def scan_based(self, part_result, cycle_result, slot_result):
+        point_result, sequence_result = [], []
+
+        class Mount:
+            def __init__(self):
+                self.pos = []
+                self.angle = []
+
+                self.part = []
+                self.step = []
+
+            def pop(self, index):
+                self.pos.pop(index)
+                self.angle.pop(index)
+
+                self.part.pop(index)
+                self.step.pop(index)
+
+        all_points = Mount()
+
+        for step_index, data in self.step_data.iterrows():
+            part_index = self.part_data[self.part_data.part == data.part].index.tolist()[0]
+
+            # 记录贴装点序号索引和对应的位置坐标
+            all_points.pos.append(Point(data.x + self.config.stopper_pos.x, data.y + self.config.stopper_pos.y))
+            all_points.angle.append(data.r)
+            all_points.part.append(part_index)
+            all_points.step.append(step_index)
+
+        head_num = self.config.head_num
+        left_boundary, right_boundary = min(all_points.pos, key=lambda p: p.x).x, \
+                                        max(all_points.pos, key=lambda p: p.x).x
+        search_step = max((right_boundary - left_boundary) / head_num / 2, 0)
+
+        ref_pos_y = min(all_points.pos, key=lambda p: p.y).y
+        for cycle_index, component_cycle in enumerate(part_result):
+            for _ in range(cycle_result[cycle_index]):
+                min_dist = np.inf
+                tmp_assigned_point, tmp_assigned_head_seq = [], []
+
+                tmp_all_points = Mount()
+                for search_dir in range(3):     # 不同的搜索方向，贴装头和起始点的选取方法各不相同
+                    if search_dir == 0:
+                        # 从左向右搜索
+                        search_points = np.arange(left_boundary, (left_boundary + right_boundary) / 2, search_step)
+                        head_range = list(range(head_num))
+                    elif search_dir == 1:
+                        # 从右向左搜索
+                        search_points = np.arange(right_boundary + 1e-3, (left_boundary + right_boundary) / 2, -search_step)
+                        head_range = list(range(head_num - 1, -1, -1))
+                    else:
+                        # 从中间向两边搜索
+                        search_points = np.arange(left_boundary, right_boundary, search_step / 2)
+                        head_range, head_index = [], (head_num - 1) // 2
+                        while head_index >= 0:
+                            if 2 * head_index != head_num - 1:
+                                head_range.append(head_num - 1 - head_index)
+                            head_range.append(head_index)
+                            head_index -= 1
+
+                    for start_points in search_points:
+                        cur_all_points = copy.deepcopy(all_points)
+
+                        assigned_point = [-1] * head_num
+                        assigned_mount_point, assigned_mount_angle = [Point(0, 0)] * head_num, [0] * head_num
+                        head_counter, point_index = 0, -1
+                        for head_index in head_range:
+                            if head_counter == 0:
+                                part_index = part_result[cycle_index][head_index]
+
+                                if part_index == -1:
+                                    continue
+
+                                min_horizontal_distance = np.inf
+                                for index, part in enumerate(cur_all_points.part):
+                                    if part != part_result[cycle_index][head_index]:
+                                        continue
+
+                                    horizontal_distance = abs(cur_all_points.pos[index].x - start_points) + 0 * abs(
+                                        cur_all_points.pos[index].y - ref_pos_y)
+
+                                    if horizontal_distance < min_horizontal_distance:
+                                        min_horizontal_distance = horizontal_distance
+                                        point_index = index
+                            else:
+                                point_index = -1
+                                min_cheby_distance = np.inf
+
+                                for index, part in enumerate(cur_all_points.part):
+                                    if part != part_result[cycle_index][head_index]:
+                                        continue
+                                    point_pos = [Point(cur_all_points.pos[index].x - head_index * self.config.head_intv,
+                                                       cur_all_points.pos[index].y)]
+
+                                    cheby_distance, euler_distance = 0, 0
+                                    for next_head in range(head_num):
+                                        if assigned_point[next_head] == -1:
+                                            continue
+                                        point_pos.append(Point(assigned_mount_point[next_head].x - next_head * head_num,
+                                                               assigned_mount_point[next_head].y))
+
+                                    point_pos = sorted(point_pos, key=lambda p: p.x)
+                                    for mount_seq in range(len(point_pos) - 1):
+                                        delta_x = axis_moving_time(
+                                            point_pos[mount_seq].x - point_pos[mount_seq + 1].x, 0)
+                                        delta_y = axis_moving_time(
+                                            point_pos[mount_seq].y - point_pos[mount_seq + 1].y, 1)
+                                        cheby_distance += max(delta_x, delta_y)
+                                        euler_distance += math.sqrt(delta_x ** 2 + delta_y ** 2)
+
+                                    # cheby_distance += 0.01 * euler_distance
+                                    if cheby_distance < min_cheby_distance:
+                                        min_cheby_distance, min_euler_distance = cheby_distance, euler_distance
+                                        point_index = index
+
+                            if point_index == -1:
+                                continue
+
+                            head_counter += 1
+
+                            assigned_point[head_index] = all_points.step[point_index]
+                            assigned_mount_point[head_index] = all_points.pos[point_index]
+                            assigned_mount_angle[head_index] = all_points.angle[point_index]
+
+                            cur_all_points.pop(point_index)
+
+                        dist, head_seq = self.dynamic_programming_cycle_path(assigned_point, slot_result[cycle_index])
+
+                        if min_dist is None or dist < min_dist:
+                            tmp_all_points = cur_all_points
+                            tmp_assigned_point, tmp_assigned_head_seq = assigned_point, head_seq
+                            min_dist = dist
+
+                all_points = tmp_all_points
+                point_result.append(tmp_assigned_point)
+                sequence_result.append(tmp_assigned_head_seq)
+
+        return point_result, sequence_result
+
+    def greedy_cluster(self, part_result, cycle_result, slot_result):
+        point_result, sequence_result = [], []
+
+        # === assign CT group to feeder slot ===
+        component_point_pos = defaultdict(list)
+
+        for idx, data in self.step_data.iterrows():
+            component_point_pos[data.part].append([data.x + self.config.stopper_pos.x,
+                                                   data.y + self.config.stopper_pos.y, idx])
+
+        for pos_list in component_point_pos.values():
+            pos_list.sort(key=lambda x: (x[0], x[1]))
+
+        component_point_index = defaultdict(int)
+        for cycle_set in range(len(cycle_result)):
+            for cycle in range(cycle_result[cycle_set]):
+                point_result.append([-1 for _ in range(self.config.head_num)])
+                for head in range(self.config.head_num):
+                    part_index = part_result[cycle_set][head]
+                    if part_index == -1:
+                        continue
+
+                    part = self.part_data.loc[part_index]['part']
+                    point_info = component_point_pos[part][component_point_index[part]]
+
+                    point_result[-1][head] = point_info[2]
+                    # mount_point[head] = point_info[0:2]
+
+                    component_point_index[part] += 1
+                sequence_result.append(
+                    self.dynamic_programming_cycle_path(point_result[-1], slot_result[cycle_set])[1])
+        return point_result, sequence_result
+
+    def greedy_level_placing(self, part_result, cycle_result, slot_result):
+        point_result, sequence_result = [], []
+        part_indices = defaultdict(int)
+        for part_idx, data in self.part_data.iterrows():
+            part_indices[data.part] = part_idx
+
+        mount_point_pos = defaultdict(list)
+        for pcb_idx, data in self.step_data.iterrows():
+            mount_point_pos[part_indices[data.part]].append([data.x, data.y, pcb_idx])
+
+        for index_ in mount_point_pos.keys():
+            mount_point_pos[index_].sort(key=lambda x: (x[1], x[0]))
+
+        for cycle_idx, _ in enumerate(cycle_result):
+            for _ in range(cycle_result[cycle_idx]):
+                point_result.append([-1 for _ in range(self.config.head_num)])
+                for head in range(self.config.head_num):
+                    if part_result[cycle_idx][head] == -1:
+                        continue
+                    index_ = part_result[cycle_idx][head]
+                    point_result[-1][head] = mount_point_pos[index_][-1][2]
+                    mount_point_pos[index_].pop()
+                sequence_result.append(self.dynamic_programming_cycle_path(point_result[-1], slot_result[cycle_idx])[1])
+        return point_result, sequence_result
+
diff --git a/opt/smm/two_phase.py b/opt/smm/two_phase.py
new file mode 100644
index 0000000..be70194
--- /dev/null
+++ b/opt/smm/two_phase.py
@@ -0,0 +1,484 @@
+import copy
+
+from opt.smm.basis import *
+from opt.utils import *
+from opt.smm.solver import *
+
+
+class TwoPhaseOpt(BaseOpt):
+    def __init__(self, config, part_data, step_data, feeder_data=pd.DataFrame(columns=['slot', 'part'])):
+        super().__init__(config, part_data, step_data, feeder_data)
+        self.feeder_assigner = FeederAssignOpt(config, part_data, step_data)
+        self.reduction = True
+        self.partition = True
+        self.initial = False
+        
+    def optimize(self, hinter=True):
+        # data preparation: convert data to index
+        part_list, nozzle_list = defaultdict(int), defaultdict(int)
+        part_feeder = defaultdict(int)
+        cpidx_2_part, nzidx_2_nozzle, cpidx_2_nzidx = {}, {}, {}
+        arg_slot_rng = None if len(self.feeder_data) == 0 else [self.feeder_data.iloc[0].slot, self.feeder_data.iloc[-1].slot]
+        for idx, data in self.part_data.iterrows():
+            part, nozzle = data.part, data.nz
+
+            cpidx_2_part[idx] = part
+            nz_key = [key for key, val in nzidx_2_nozzle.items() if val == nozzle]
+
+            nz_idx = len(nzidx_2_nozzle) if len(nz_key) == 0 else nz_key[0]
+            nzidx_2_nozzle[nz_idx] = nozzle
+
+            part_list[part] = 0
+            part_feeder[part] = data.fdn
+            cpidx_2_nzidx[idx] = nz_idx
+
+        for _, data in self.step_data.iterrows():
+            idx = self.part_data[self.part_data.part == data.part].index.tolist()[0]
+            nozzle = self.part_data.loc[idx].nz
+
+            nozzle_list[nozzle] += 1
+            part_list[data.part] += 1
+
+        part_feederbase = defaultdict(int)
+        if self.feeder_data is not None:
+            for _, data in self.feeder_data.iterrows():
+                idx = -1
+                for idx, part_ in cpidx_2_part.items():
+                    if data.part == part_:
+                        break
+                assert idx != -1
+                part_feederbase[idx] = data.slot  # part index - slot
+
+        ratio = 1 if self.reduction else 2
+        I, J = len(cpidx_2_part.keys()), len(nzidx_2_nozzle.keys())
+        # === determine the hyper-parameter of L ===
+        # first phase: calculate the number of heads for each type of nozzle
+        nozzle_heads = defaultdict(int)
+        for nozzle in nozzle_list.keys():
+            nozzle_heads[nozzle] = 1
+            
+        head_num = self.config.head_num
+        while sum(nozzle_heads.values()) != head_num:
+            max_cycle_nozzle = None
+
+            for nozzle, head_num in nozzle_heads.items():
+                if max_cycle_nozzle is None or nozzle_list[nozzle] / head_num > nozzle_list[max_cycle_nozzle] / \
+                        nozzle_heads[max_cycle_nozzle]:
+                    max_cycle_nozzle = nozzle
+
+            assert max_cycle_nozzle is not None
+            nozzle_heads[max_cycle_nozzle] += 1
+
+        nozzle_comp_points = defaultdict(list)
+        for part, points in part_list.items():
+            idx = self.part_data[self.part_data.part == part].index.tolist()[0]
+            nozzle = self.part_data.loc[idx].nz
+            nozzle_comp_points[nozzle].append([part, points])
+
+        level = 1 if len(part_list) == 1 or len(part_list) % head_num == 0 else 2
+        part_assignment, cycle_assignment = [], []
+
+        def aux_func(info):
+            return max(map(lambda points: max([p[1] for p in points]), info))
+
+        pre_objbst, pre_changetime = None, None
+
+        def terminate_condition(mdl, where):
+            if where == GRB.Callback.MIP:
+                objbst, objbnd = mdl.cbGet(GRB.Callback.MIP_OBJBST), mdl.cbGet(GRB.Callback.MIP_OBJBND)
+                changetime = mdl.cbGet(GRB.Callback.RUNTIME)
+                nonlocal pre_objbst, pre_changetime
+                #            condition: value change
+                if abs(objbst - 1e+100) > 1:  # 閬垮厤鏈壘鍒板彲琛岃В鎻愬墠閫�鍑�
+                    if pre_objbst and abs(pre_objbst - objbst) < 1e-3:
+                        if pre_changetime and changetime - pre_changetime > 90 * (1 - objbnd / objbst):
+                            mdl.terminate()
+                    else:
+                        pre_changetime = changetime
+
+                pre_objbst = objbst
+
+        def recursive_assign(assign_points, nozzle_compo_points, cur_level, total_level) -> int:
+            def func(points):
+                return map(lambda points: max([p[1] for p in points]), points)
+
+            if cur_level > total_level and sum(func(nozzle_compo_points.values())) == 0:
+                return 0
+            elif assign_points <= 0 and cur_level == 1:
+                return -1  # backtrack
+            elif assign_points <= 0 or cur_level > total_level:
+                return 1  # fail
+
+            nozzle_compo_points_cpy = copy.deepcopy(nozzle_compo_points)
+            prev_assign = 0
+            for part in part_assignment[cur_level - 1]:
+                if part != -1:
+                    prev_assign += 1
+
+            head_idx = 0
+            for nozzle, head in nozzle_heads.items():
+                while head:
+                    min_idx = -1
+                    for idx, (part, points) in enumerate(nozzle_compo_points_cpy[nozzle]):
+                        if points >= assign_points and (
+                                min_idx == -1 or points < nozzle_compo_points_cpy[nozzle][min_idx][1]):
+                            min_idx = idx
+                    part_assignment[cur_level - 1][head_idx] = -1 if min_idx == -1 else \
+                        nozzle_compo_points_cpy[nozzle][min_idx][0]
+                    if min_idx != -1:
+                        nozzle_compo_points_cpy[nozzle][min_idx][1] -= assign_points
+                    head -= 1
+                    head_idx += 1
+
+            cycle_assignment[cur_level - 1] = assign_points
+            for part in part_assignment[cur_level - 1]:
+                if part != -1:
+                    prev_assign -= 1
+
+            if prev_assign == 0:
+                res = 1
+            else:
+                points = min(len(self.step_data) // head_num + 1, aux_func(nozzle_compo_points_cpy.values()))
+                res = recursive_assign(points, nozzle_compo_points_cpy, cur_level + 1, total_level)
+            if res == 0:
+                return 0
+            elif res == 1:
+                # All cycles have been completed, but there are still points left to be allocated
+                return recursive_assign(assign_points - 1, nozzle_compo_points, cur_level, total_level)
+
+        # second phase: (greedy) recursive search to assign points for each cycle set and obtain an initial solution
+        while True:
+            part_assignment = [[-1 for _ in range(head_num)] for _ in range(level)]
+            cycle_assignment = [-1 for _ in range(level)]
+            points = min(len(self.step_data) // head_num + 1, max(part_list.values()))
+            if recursive_assign(points, nozzle_comp_points, 1, level) == 0:
+                break
+            level += 1
+
+        L = len(cycle_assignment) if self.partition else len(self.step_data)
+        S = ratio * sum(part_feeder.values()) if len(self.feeder_data) == 0 else arg_slot_rng[-1] - arg_slot_rng[
+            0] + 1  # the available feeder num
+        M = len(self.step_data)  # a sufficiently large number (number of placement points)
+        HC = [[0 for _ in range(J)] for _ in range(I)]
+        for i in range(I):
+            for j in range(J):
+                HC[i][j] = 1 if cpidx_2_nzidx[i] == j else 0
+
+        mdl = Model('SMT')
+        mdl.setParam('Seed', 0)
+        mdl.setParam('OutputFlag', hinter)  # set whether output the debug information
+        mdl.setParam('TimeLimit', 3600)
+        mdl.setParam('PoolSearchMode', 2)
+        mdl.setParam('PoolSolutions', 100)
+        mdl.setParam('PoolGap', 1e-4)
+        mdl.setParam("Heuristics", 0.5)
+
+        # Use only if other methods, including exploring the tree with the default settings, do not yield a viable solution
+        # mdl.setParam("ZeroObjNodes", 100)
+
+        # === Decision Variables ===
+        H = head_num
+        x = mdl.addVars(I, S, H, L, vtype=GRB.BINARY, name='x')
+        y = mdl.addVars(I, H, L, vtype=GRB.BINARY, name='y')
+        v = mdl.addVars(S, H, L, vtype=GRB.BINARY, name='v')
+        c = mdl.addVars(I, H, L, vtype=GRB.INTEGER, name='c')
+
+        mdl.addConstrs(c[i, h, l] <= part_list[cpidx_2_part[i]] for i in range(I) for h in range(H) for l in range(L))
+
+        f = {}
+        for i in range(I):
+            if i not in part_feederbase.keys():
+                for s in range(S):
+                    f[s, i] = mdl.addVar(vtype=GRB.BINARY, name='f_' + str(s) + '_' + str(i))
+            else:
+                for s in range(S):
+                    f[s, i] = 1 if part_feederbase[i] == s + arg_slot_rng[0] else 0
+
+        p = mdl.addVars(S + (H - 1) * ratio, L, vtype=GRB.BINARY, name='p')
+        z = mdl.addVars(J, H, L, vtype=GRB.BINARY)
+
+        d = mdl.addVars(L, H, vtype=GRB.INTEGER, name='d')
+        d_plus = mdl.addVars(J, H, L, vtype=GRB.INTEGER, name='d_plus')
+        d_minus = mdl.addVars(J, H, L, vtype=GRB.INTEGER, name='d_minus')
+
+        max_cycle = math.ceil(len(self.step_data) / H)
+        PU = mdl.addVars(S + (H - 1) * ratio, L, vtype=GRB.INTEGER, name='PU')
+        WL = mdl.addVars(L, vtype=GRB.INTEGER, ub=max_cycle, name='WL')
+        NC = mdl.addVars(H, vtype=GRB.INTEGER, name='NC')
+
+        part_2_cpidx = defaultdict(int)
+        for idx, part in cpidx_2_part.items():
+            part_2_cpidx[part] = idx
+
+        if self.initial:
+            # initial some variables to speed up the search process
+            # ensure the priority of the workload assignment
+            cycle_index = sorted(range(len(cycle_assignment)), key=lambda k: cycle_assignment[k], reverse=True)
+            part_list = []
+
+            for cycle in cycle_index:
+                cycle_part = part_assignment[cycle]
+                for part in cycle_part:
+                    if part != -1 and part not in part_list:
+                        part_list.append(part)
+            slot = 0
+            for part in part_list:
+                if self.feeder_data is not None:
+                    while slot in self.feeder_data.keys():
+                        slot += 1  # skip assigned feeder slot
+
+                if part_2_cpidx[part] in part_feederbase.keys():
+                    continue
+
+                part_feederbase[part_2_cpidx[part]] = slot
+                f[slot, part_2_cpidx[part]].Start = 1
+                slot += 1
+
+            for idx, cycle in enumerate(cycle_index):
+                WL[idx].Start = cycle_assignment[cycle]
+                for h in range(H):
+                    part = part_assignment[cycle][h]
+                    if part == -1:
+                        continue
+                    i = part_2_cpidx[part]
+                    y[i, h, idx].Start = 1
+                    v[part_feederbase[i], h, idx].Start = 1
+
+        # === Objective ===
+        mdl.setObjective(self.cycle_weight * quicksum(WL[l] for l in range(L)) +
+                         2 * self.nozzle_change_weight * quicksum(NC[h] for h in range(H)) +
+                         self.pickup_weight * quicksum(PU[s, l] for s in range(-(H - 1) * ratio, S) for l in range(L)))
+
+        # === Constraint ===
+        if not self.partition:
+            mdl.addConstrs(WL[l] <= 1 for l in range(L))
+
+        # work completion
+        mdl.addConstrs(c[i, h, l] == WL[l] * y[i, h, l] for i in range(I) for h in range(H) for l in range(L))
+        mdl.addConstrs(c[i, h, l] <= max_cycle * y[i, h, l] for i in range(I) for h in range(H) for l in range(L))
+        mdl.addConstrs(c[i, h, l] <= WL[l] for i in range(I) for h in range(H) for l in range(L))
+        mdl.addConstrs(c[i, h, l] >= WL[l] - max_cycle * (1 - y[i, h, l]) for i in range(I) for h in range(H)
+                       for l in range(L))
+
+        mdl.addConstrs(quicksum(c[i, h, l] for h in range(H) for l in range(L)) == part_list[cpidx_2_part[i]]
+                       for i in range(I))
+
+        # variable constraint
+        mdl.addConstrs(quicksum(y[i, h, l] for i in range(I)) <= 1 for h in range(H) for l in range(L))
+
+        # simultaneous pick
+        for s in range(S + (H - 1) * ratio):
+            rng = list(range(max(0, -math.floor(s / ratio)), min(H, math.ceil((S - s) / ratio))))
+            for l in range(L):
+                mdl.addConstr(quicksum(v[s + h * ratio, h, l] for h in rng) <= H * p[s, l])
+                mdl.addConstr(quicksum(v[s + h * ratio, h, l] for h in rng) >= p[s, l])
+
+        mdl.addConstrs(PU[s, l] == p[s, l] * WL[l] for s in range(S + (H - 1) * ratio) for l in range(L))
+        # mdl.addConstrs(PU[s, l] <= max_cycle * p[s, l] for s in range(S + (H - 1) * ratio) for l in range(L))
+        # mdl.addConstrs(PU[s, l] <= WL[l] for s in range(-(H - 1) * ratio, S) for l in range(L))
+        # mdl.addConstrs(PU[s, l] >= WL[l] - max_cycle * (1 - p[s, l]) for s in range(-(H - 1) * ratio, S) for l in
+        #                range(L))
+
+        # nozzle change
+        mdl.addConstrs(
+            z[j, h, l] - z[j, h, l + 1] == d_plus[j, h, l] - d_minus[j, h, l] for l in range(L - 1) for j in range(J)
+            for h in range(H))
+
+        mdl.addConstrs(z[j, h, 0] - z[j, h, L - 1] == d_plus[j, h, L - 1] - d_minus[j, h, L - 1] for j in range(J)
+                       for h in range(H))
+
+        mdl.addConstrs(
+            2 * d[l, h] == quicksum(d_plus[j, h, l] for j in range(J)) + quicksum(d_minus[j, h, l] for j in range(J))
+            for l in range(L) for h in range(H))
+
+        mdl.addConstrs(NC[h] == quicksum(d[l, h] for l in range(L)) for h in range(H))
+        mdl.addConstrs(quicksum(y[i, h, l] for i in range(I) for h in range(H)) * M >= WL[l] for l in range(L))
+
+        # nozzle-component compatibility
+        mdl.addConstrs(y[i, h, l] <= quicksum(HC[i][j] * z[j, h, l] for j in range(J)) for i in range(I) for h in
+                       range(H) for l in range(L))
+
+        # available number of feeder
+        mdl.addConstrs(quicksum(f[s, i] for s in range(S)) <= part_feeder[cpidx_2_part[i]] for i in range(I))
+
+        # available number of nozzle
+        mdl.addConstrs(quicksum(z[j, h, l] for h in range(H)) <= H for j in range(J) for l in range(L))
+
+        # upper limit for occupation for feeder slot
+        mdl.addConstrs(quicksum(f[s, i] for i in range(I)) <= 1 for s in range(S))
+        mdl.addConstrs(quicksum(v[s, h, l] for s in range(S)) >= quicksum(y[i, h, l] for i in range(I)) for h in
+                       range(H) for l in range(L))
+
+        # others
+        mdl.addConstrs(quicksum(z[j, h, l] for j in range(J)) <= 1 for h in range(H) for l in range(L))
+        mdl.addConstrs(quicksum(x[i, s, h, l] for h in range(H) for l in range(L)) >= f[s, i] for i in range(I)
+                       for s in range(S))
+        mdl.addConstrs(quicksum(x[i, s, h, l] for h in range(H) for l in range(L)) <= M * f[s, i] for i in
+                       range(I) for s in range(S))
+
+        # mdl.addConstrs(f[s, i] >= x[i, s, h, l] for s in range(S) for i in range(I) for h in range(H) for l in range(L))
+        #
+        # mdl.addConstrs(quicksum(x[i, s, h, l] for h in range(H) for l in range(L)) >= f[s, i] for s in
+        #                range(S) for i in range(I))
+
+        # the constraints to speed up the search process
+        mdl.addConstrs(quicksum(x[i, s, h, l] for i in range(I) for s in range(S)) <= 1 for h in range(H) for l
+                       in range(L))
+
+        if self.reduction:
+            mdl.addConstrs(WL[l] >= WL[l + 1] for l in range(L - 1))
+            mdl.addConstr(quicksum(WL[l] for l in range(L)) <= sum(cycle_assignment))
+            mdl.addConstr(quicksum(WL[l] for l in range(L)) >= math.ceil(len(self.step_data) / H))
+            mdl.addConstrs(quicksum(z[j, h, l] for j in range(J) for h in range(H)) >= quicksum(
+                z[j, h, l + 1] for j in range(J) for h in range(H)) for l in range(L - 1))
+
+        mdl.addConstrs(y[i, h, l] <= WL[l] for i in range(I) for h in range(H) for l in range(L))
+        mdl.addConstrs(v[s, h, l] <= WL[l] for s in range(S) for h in range(H) for l in range(L))
+
+        mdl.addConstrs(x[i, s, h, l] >= y[i, h, l] + v[s, h, l] - 1 for i in range(I) for s in range(S) for h in range(H)
+                       for l in range(L))
+        mdl.addConstrs(x[i, s, h, l] <= y[i, h, l] for i in range(I) for s in range(S) for h in range(H)
+                       for l in range(L))
+
+        mdl.addConstrs(x[i, s, h, l] <= v[s, h, l] for i in range(I) for s in range(S) for h in range(H)
+                       for l in range(L))
+
+        # === search process ===
+        # mdl.update()
+        # mdl.write('mdl.lp')
+
+        mdl.optimize(terminate_condition)
+        # mdl.optimize()
+
+        # === result generation ===
+        opt_res_list = defaultdict(OptResult)
+        if mdl.Status == GRB.OPTIMAL or mdl.Status == GRB.INTERRUPTED or mdl.Status == GRB.TIME_LIMIT:
+            # === selection from solution pool ===
+            component_pos = defaultdict(list[Point])
+            for _, data in self.step_data.iterrows():
+                component_index = self.part_data[self.part_data.part == data.part].index.tolist()[0]
+                component_pos[component_index].append(Point(data.x, data.y))
+
+            for part in component_pos.keys():
+                component_pos[part] = sorted(component_pos[part], key=lambda pos: (pos.x, pos.y))
+
+            for sol_counter in range(mdl.SolCount):
+                mdl.Params.SolutionNumber = sol_counter
+                opt_res = OptResult()
+                # == 杞崲鏍囧噯鐨勮创瑁呭ご鍒嗛厤鐨勮В ===
+                for l in range(L):
+                    if abs(WL[l].Xn) <= 1e-4:
+                        continue
+                    opt_res.cycle.append(round(WL[l].Xn))
+                    opt_res.part.append([-1] * head_num)
+                    opt_res.slot.append([-1] * head_num)
+
+                    for h in range(head_num):
+                        for i in range(I):
+                            if abs(y[i, h, l].Xn) <= 1e-4:
+                                continue
+                            opt_res.part[-1][h] = i
+
+                        for s in range(S):
+                            if abs(v[s, h, l].Xn - 1) < 1e-4 and opt_res.part[-1][h] != -1:
+                                opt_res.slot[-1][h] = s
+
+                # 鏍规嵁璐磋澶翠綅缃紝杞崲渚涙枡鍣ㄦЫ浣�
+                cp_avg_head, cp_sum_cycle = defaultdict(float), defaultdict(int)
+                for cycle, component_assign in enumerate(opt_res.part):
+                    for head, part in enumerate(component_assign):
+                        if part == -1:
+                            continue
+                        cp_avg_head[part] += opt_res.cycle[cycle] * head
+                        cp_sum_cycle[part] += opt_res.cycle[cycle]
+
+                for part, head in cp_avg_head.items():
+                    cp_avg_head[part] = head / cp_sum_cycle[part]
+
+                avg_position = sum([data.x - cp_avg_head[part_2_cpidx[data.part]] * self.config.head_intv for _, data in
+                                    self.step_data.iterrows()]) / len(self.step_data)
+                avg_slot = 0
+                D_PU, D_PL, D_BW, D_FW = 0, 0, 0, 0
+                for cycle, slots in enumerate(opt_res.slot):
+                    min_slot, max_slot = self.config.slot_num, 0
+                    for head, slot in enumerate(slots):
+                        if slot == -1:
+                            continue
+                        min_slot = min(min_slot, slot - head * ratio)
+                        max_slot = max(max_slot, slot - head * ratio)
+                    avg_slot += (max_slot - min_slot) * opt_res.cycle[cycle]
+                    D_PU += (max_slot - min_slot) * self.config.slot_intv * opt_res.cycle[cycle]  # 鎷惧彇璺緞
+
+                avg_slot /= sum(opt_res.cycle)
+                start_slot = round((avg_position + self.config.stopper_pos.x - self.config.slotf1_pos.x)
+                                   / self.config.slot_intv + avg_slot / 2) + 1
+
+                for cycle in range(len(opt_res.slot)):
+                    for head in range(head_num):
+                        if (slot := opt_res.slot[cycle][head]) == -1:
+                            continue
+                        opt_res.slot[cycle][head] = start_slot + slot * (2 if ratio == 1 else 1)
+
+                component_pos_counter = defaultdict(int)
+                cycle_place_pos = defaultdict(list[Point])
+                for head in range(head_num):
+                    for cycle in range(len(opt_res.cycle)):
+                        if (part := opt_res.part[cycle][head]) == -1:
+                            continue
+
+                        avg_place_pos = Point(0, 0, _h=head)
+                        for counter in range(round(opt_res.cycle[cycle])):
+                            avg_place_pos.x = (1 - 1.0 / (counter + 1)) * avg_place_pos.x + (
+                                    component_pos[part][component_pos_counter[part]].x - head * self.config.head_intv) / \
+                                              (counter + 1)
+                            avg_place_pos.y = (1 - 1.0 / (counter + 1)) * avg_place_pos.y + component_pos[part][
+                                component_pos_counter[part]].y / (counter + 1)
+                            component_pos_counter[part] += 1
+                        avg_place_pos.x += self.config.stopper_pos.x
+                        avg_place_pos.y += self.config.stopper_pos.y
+                        cycle_place_pos[cycle].append(avg_place_pos)
+
+                intv_ratio = self.config.head_intv // self.config.slot_intv
+                for cycle in range(len(opt_res.cycle)):
+                    min_slot, max_slot = self.config.slot_num, 0
+                    for head in range(head_num):
+                        if (slot := opt_res.slot[cycle][head]) == -1:
+                            continue
+                        min_slot = min(min_slot, slot - head * intv_ratio)
+                        max_slot = max(max_slot, slot - head * intv_ratio)
+                    # cycle_place_pos[cycle] = sorted(cycle_place_pos[cycle], key=lambda pt: pt.x)
+
+                    pick_pos = copy.deepcopy(self.config.slotf1_pos)
+                    pick_pos.x += (min_slot + max_slot) / 2 * self.config.slot_intv
+                    _, seq = self.path_planner.dynamic_programming_cycle_path(cycle_place_pos[cycle], pick_pos)
+                    head_position = [Point(0, 0) for _ in range(head_num)]
+                    for point in cycle_place_pos[cycle]:
+                        head_position[point.h] = point
+
+                    for idx in range(len(seq) - 1):
+                        h1, h2 = seq[idx], seq[idx + 1]
+                        D_PL += max(abs(head_position[h1].x - head_position[h2].x),
+                                    abs(head_position[h1].y - head_position[h2].y)) * opt_res.cycle[cycle]
+
+                opt_res_list[sol_counter] = opt_res
+
+        solution_number = 0
+        # mdl.Params.SolutionNumber = 0
+        if hinter:
+            print('total cost = {}'.format(mdl.objval))
+            print('cycle = {}, nozzle change = {}, pick up = {}'.format(quicksum(WL[l].Xn for l in range(L)), quicksum(
+                NC[h].Xn for h in range(head_num)), quicksum(
+                PU[s, l].Xn for s in range(-(head_num - 1) * ratio, S) for l in range(L))))
+
+            print('workload: ')
+            for l in range(L):
+                print(WL[l].Xn, end=', ')
+
+            print('')
+            print('result')
+            print('component assignment: ', opt_res_list[solution_number].part)
+            print('feeder assignment: ', opt_res_list[solution_number].slot)
+            print('cycle assignment: ', opt_res_list[solution_number].cycle)
+
+        self.result = opt_res_list[solution_number]
+
diff --git a/opt/utils.py b/opt/utils.py
new file mode 100644
index 0000000..bd252f8
--- /dev/null
+++ b/opt/utils.py
@@ -0,0 +1,279 @@
+from core.common import *
+from data.type import *
+
+
+head_rotary_velocity = 8e-5  # 贴装头R轴旋转时间
+x_max_velocity, y_max_velocity = 1.4, 1.2
+x_max_acceleration, y_max_acceleration = x_max_velocity / 0.079, y_max_velocity / 0.079
+
+
+def axis_moving_time(distance, axis=0):
+    assert 0 <= axis <= 2
+    distance = abs(distance) * 1e-3
+    Lamax = x_max_velocity ** 2 / x_max_acceleration if axis == 0 else y_max_velocity ** 2 / y_max_acceleration
+    Tmax = x_max_velocity / x_max_acceleration if axis == 0 else y_max_velocity / y_max_acceleration
+    if axis == 0:
+        return 2 * math.sqrt(distance / x_max_acceleration) if distance < Lamax else 2 * Tmax + (
+                    distance - Lamax) / x_max_velocity
+    elif axis == 1:
+        return 2 * math.sqrt(distance / y_max_acceleration) if distance < Lamax else 2 * Tmax + (
+                    distance - Lamax) / y_max_velocity
+    elif axis == 2:
+        return abs((distance + 180) % 360 - 180) * head_rotary_velocity
+    return .0
+
+
+def optimizer_result_hinter(config, part_data, opt_res, nozzle_hinter=False, part_hinter=False,
+                            slot_hinter=False, place_hinter=False):
+    if nozzle_hinter:
+        columns = ['H{}'.format(i + 1) for i in range(config.head_num)] + ['cycle']
+
+        nozzle_assign = pd.DataFrame(columns=columns)
+        for cycle, components in enumerate(opt_res.part):
+            nozzle_assign_row = len(nozzle_assign)
+            nozzle_assign.loc[nozzle_assign_row, 'cycle'] = opt_res.cycle[cycle]
+
+            for head in range(config.head_num):
+                index = opt_res.part[cycle][head]
+                if index == -1:
+                    nozzle_assign.loc[nozzle_assign_row, 'H{}'.format(head + 1)] = ''
+                else:
+                    nozzle = part_data.loc[index]['nz']
+                    nozzle_assign.loc[nozzle_assign_row, 'H{}'.format(head + 1)] = nozzle
+
+            for head in range(config.head_num):
+                if nozzle_assign_row == 0 or nozzle_assign.loc[nozzle_assign_row - 1, 'H{}'.format(head + 1)] != \
+                        nozzle_assign.loc[nozzle_assign_row, 'H{}'.format(head + 1)]:
+                    break
+            else:
+                nozzle_assign.loc[nozzle_assign_row - 1, 'cycle'] += nozzle_assign.loc[nozzle_assign_row, 'cycle']
+                nozzle_assign.drop([len(nozzle_assign) - 1], inplace=True)
+
+        print(nozzle_assign)
+        print('')
+
+    if part_hinter:
+        columns = ['H{}'.format(i + 1) for i in range(config.head_num)] + ['cycle']
+
+        part_assign = pd.DataFrame(columns=columns)
+        for cycle, components in enumerate(opt_res.part):
+            part_assign.loc[cycle, 'cycle'] = opt_res.cycle[cycle]
+            for head in range(config.head_num):
+                index = opt_res.part[cycle][head]
+                if index == -1:
+                    part_assign.loc[cycle, 'H{}'.format(head + 1)] = ''
+                else:
+                    # component_assign.loc[cycle, 'H{}'.format(head + 1)] = component_data.loc[index]['part']
+                    part_assign.loc[cycle, 'H{}'.format(head + 1)] = 'C' + str(index)
+
+        print(part_assign)
+        print('')
+
+    if slot_hinter:
+        columns = ['H{}'.format(i + 1) for i in range(config.head_num)] + ['cycle']
+
+        slot_assign = pd.DataFrame(columns=columns)
+        for cycle, components in enumerate(opt_res.slot):
+            slot_assign.loc[cycle, 'cycle'] = opt_res.cycle[cycle]
+            for head in range(config.head_num):
+                slot = opt_res.slot[cycle][head]
+                if slot == -1:
+                    slot_assign.loc[cycle, 'H{}'.format(head + 1)] = 'A'
+                else:
+                    try:
+                        slot_assign.loc[cycle, 'H{}'.format(head + 1)] = 'F{}'.format(
+                            slot) if slot <= config.slot_num // 2 else 'R{}'.format(slot - config.head_num)
+                    except:
+                        print('')
+
+        print(slot_assign)
+        print('')
+
+    if place_hinter:
+        columns = ['H{}'.format(i + 1) for i in range(config.head_num)] + ['cycle']
+
+        place_assign = pd.DataFrame(columns=columns)
+        for cycle, _ in enumerate(opt_res.point):
+            place_assign.loc[cycle, 'cycle'] = 1
+            for head in range(config.head_num):
+                point = opt_res.point[cycle][head]
+                if point != -1:
+                    place_assign.loc[cycle, 'H{}'.format(head + 1)] = 'P{}'.format(point)
+                else:
+                    place_assign.loc[cycle, 'H{}'.format(head + 1)] = ''
+
+        headseq_assign = pd.DataFrame(columns=columns)
+        for cycle, headseq in enumerate(opt_res.sequence):
+            headseq_assign.loc[cycle, 'cycle'] = 1
+            for head in range(len(headseq)):
+                headseq_assign.loc[cycle, 'H{}'.format(head + 1)] = 'H{}'.format(headseq[head])
+
+        print(place_assign)
+        print(headseq_assign)
+        print('')
+
+
+def evaluation(config: MachineConfig, part_data, step_data, opt_res: OptResult, hinter=False):
+    # === 优化结果参数 ===
+    info = OptInfo()
+    # === 校验 ===
+    info.total_points = 0
+    for cycle, part in enumerate(opt_res.part):
+        for head, component in enumerate(part):
+            if component == -1:
+                continue
+            info.total_points += opt_res.cycle[cycle]
+
+    if info.total_points != len(step_data):
+        warning_info = 'the number of ' + str(info.total_points) + ' placement point(s) is not match with the PCB data. '
+        warnings.warn(warning_info, UserWarning)
+
+    interval_ratio = config.head_intv / config.slot_intv
+    if opt_res.point:
+        total_points = info.total_points
+        for placements in opt_res.point:
+            for placement in placements:
+                if placement == -1:
+                    continue
+                total_points -= 1
+
+        if total_points != 0:
+            warnings.warn(
+                'the optimization result of component assignment result and placement result are not consistent. ',
+                UserWarning)
+            return OptInfo()
+
+    feeder_arrangement = defaultdict(set)
+    for cycle, feeder_slots in enumerate(opt_res.slot):
+        for head, slot in enumerate(feeder_slots):
+            if slot == -1:
+                continue
+            feeder_arrangement[opt_res.part[cycle][head]].add(slot)
+
+    info.total_components = len(feeder_arrangement.keys())
+    for part, data in part_data.iterrows():
+        if part in feeder_arrangement.keys() and data.fdn < len(feeder_arrangement[part]):
+            info = 'the number of arranged feeder of [' + data['part'] + '] exceeds the quantity limit'
+            warnings.warn(info, UserWarning)
+
+    cur_pos, next_pos = config.anc_pos, Point(0, 0)                         # 贴装头当前位置
+
+    # 初始化首个周期的吸嘴装配信息
+    nozzle_assigned = ['Empty' for _ in range(config.head_num)]
+    for head in range(config.head_num):
+        for cycle in range(len(opt_res.part)):
+            idx = opt_res.part[cycle][head]
+            if idx == -1:
+                continue
+            else:
+                nozzle_assigned[head] = part_data.loc[idx]['nz']
+
+    for cycle_set, _ in enumerate(opt_res.part):
+        floor_cycle, ceil_cycle = sum(opt_res.cycle[:cycle_set]), sum(opt_res.cycle[:(cycle_set + 1)])
+        for cycle in range(floor_cycle, ceil_cycle):
+            if sum(opt_res.part[cycle_set]) == -config.head_num:
+                continue
+            pick_slot, mount_pos, mount_angle = [], [], []
+            nozzle_pick_counter, nozzle_put_counter = 0, 0  # 吸嘴更换次数统计（拾取/放置分别算一次）
+            for head in range(config.head_num):
+                if opt_res.slot[cycle_set][head] != -1:
+                    pick_slot.append(opt_res.slot[cycle_set][head] - interval_ratio * head)
+                if opt_res.part[cycle_set][head] == -1:
+                    continue
+                nozzle = part_data.loc[opt_res.part[cycle_set][head]]['nz']
+                if nozzle != nozzle_assigned[head]:
+                    if nozzle_assigned[head] != 'Empty':
+                        nozzle_put_counter += 1
+                    nozzle_pick_counter += 1
+                    nozzle_assigned[head] = nozzle
+
+            # ANC处进行吸嘴更换
+            if nozzle_pick_counter + nozzle_put_counter > 0:
+                next_pos = config.anc_pos
+                move_time = max(axis_moving_time(cur_pos.x - next_pos.x, 0),
+                                axis_moving_time(cur_pos.y - next_pos.y, 1))
+                info.round_time += move_time
+                info.anc_round_counter += 1
+                info.total_distance += max(abs(cur_pos.x - next_pos.x), abs(cur_pos.y - next_pos.y))
+                cur_pos = next_pos
+
+            pick_slot = list(set(pick_slot))
+            pick_slot = sorted(pick_slot, reverse=True)
+
+            # 拾取路径(自右向左)
+            for idx, slot in enumerate(pick_slot):
+                if slot < config.slot_num // 2:
+                    next_pos = Point(config.slotf1_pos.x + config.slot_intv * (slot - 1), config.slotf1_pos.y)
+                else:
+                    next_pos = Point(config.slotr1_pos.x - config.slot_intv * (config.slot_num - slot - 1), config.slotr1_pos.y)
+                info.operation_time += config.pick_time
+                info.pickup_counter += 1
+
+                move_time = max(axis_moving_time(cur_pos.x - next_pos.x, 0),
+                                axis_moving_time(cur_pos.y - next_pos.y, 1))
+                if idx == 0:
+                    info.round_time += move_time
+                else:
+                    info.pickup_time += move_time
+
+                info.total_distance += max(abs(cur_pos.x - next_pos.x), abs(cur_pos.y - next_pos.y))
+                if slot != pick_slot[0]:
+                    info.pickup_distance += max(abs(cur_pos.x - next_pos.x), abs(cur_pos.y - next_pos.y))
+                cur_pos = next_pos
+
+            # 贴装路径
+            if opt_res.point and opt_res.sequence:
+                head_angle = [0 for _ in range(config.head_num)]
+                for head in opt_res.sequence[cycle]:
+                    index = opt_res.point[cycle][head]
+                    if index == -1:
+                        continue
+                    mount_pos.append(Point(step_data.loc[index].x - head * config.head_intv + config.stopper_pos.x,
+                                           step_data.loc[index].y + config.stopper_pos.y))
+                    head_angle[head] = step_data.loc[index]['r']
+
+                # 单独计算贴装路径
+                for cntPoints in range(len(mount_pos) - 1):
+                    info.place_distance += max(abs(mount_pos[cntPoints].x - mount_pos[cntPoints + 1].x),
+                                               abs(mount_pos[cntPoints].y - mount_pos[cntPoints + 1].y))
+
+                if mount_pos[0].x < mount_pos[-1].x:
+                    mount_pos = reversed(mount_pos)
+
+                # 考虑R轴预旋转，补偿同轴角度转动带来的额外贴装用时
+                info.operation_time += config.nozzle_install_time * nozzle_put_counter + \
+                                       config.nozzle_uninstall_time * nozzle_pick_counter
+                for idx, pos in enumerate(mount_pos):
+                    info.operation_time += config.place_time
+
+                    if idx == 0:
+                        move_time = max(axis_moving_time(cur_pos.x - pos.x, 0),
+                                        axis_moving_time(cur_pos.y - pos.y, 1))
+                        info.round_time += move_time
+                    else:
+
+                        cur_head = opt_res.sequence[cycle][idx]
+                        side_head = cur_head - 1 if cur_head % 2 else cur_head + 1
+                        if opt_res.sequence[cycle][idx - 1] != side_head:
+                            move_time = max(axis_moving_time(cur_pos.x - pos.x, 0),
+                                            axis_moving_time(cur_pos.y - pos.y, 1))
+                        else:
+                            move_time = max(axis_moving_time(cur_pos.x - pos.x, 0),
+                                            axis_moving_time(cur_pos.y - pos.y, 1),
+                                            axis_moving_time(head_angle[cur_head] - head_angle[side_head], 2))
+                        info.place_time += move_time
+
+                    info.total_distance += max(abs(cur_pos.x - pos.x), abs(cur_pos.y - pos.y))
+                    cur_pos = pos
+
+            info.nozzle_change_counter += nozzle_put_counter + nozzle_pick_counter
+
+    info.total_time = info.pickup_time + info.round_time + info.place_time + info.operation_time
+    info.cycle_counter = sum(opt_res.cycle)
+    if hinter:
+        optimizer_result_hinter(config, part_data, opt_res, part_hinter=True, nozzle_hinter=True, slot_hinter=True)
+        info.print()
+
+    return info
+
+