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修改启发式算法和遗传算法实现

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hit_lu
2023-07-20 20:02:43 +08:00
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@ -1,339 +0,0 @@
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8
base_optimizer/optimizer_common.py
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@ -49,6 +49,12 @@ feeder_width = {'SM8': (7.25, 7.25), 'SM12': (7.00, 20.00), 'SM16': (7.00, 22.00
# 可用吸嘴数量限制
nozzle_limit = {'CN065': 6, 'CN040': 6, 'CN220': 6, 'CN400': 6, 'CN140': 6}
# 时间参数
t_cycle = 0.3
t_pick, t_place = .078, .051 # 贴装/拾取用时
t_nozzle_put, t_nozzle_pick = 0.9, 0.75 # 装卸吸嘴用时
t_nozzle_change = t_nozzle_put + t_nozzle_pick
t_fix_camera_check = 0.12 # 固定相机检测时间
def axis_moving_time(distance, axis=0):
distance = abs(distance) * 1e-3
@ -880,7 +886,7 @@ def constraint_swap_mutation(component_points, individual):
offspring = individual.copy()
idx, component_index = 0, random.randint(0, len(component_points) - 1)
for points in component_points.values():
for _, points in component_points:
if component_index == 0:
while True:
index1, index2 = random.sample(range(points + max_machine_index - 2), 2)

2
base_optimizer/optimizer_feederpriority.py
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@ -2,7 +2,7 @@ from base_optimizer.optimizer_common import *
@timer_wrapper
def feeder_allocate(component_data, pcb_data, feeder_data, nozzle_pattern, figure=False):
def feeder_allocate(component_data, pcb_data, feeder_data, figure=False):
feeder_points, feeder_division_points = defaultdict(int), defaultdict(int) # 供料器贴装点数
mount_center_pos = defaultdict(int)

51
base_optimizer/optimizer_hybridgenetic.py
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@ -234,8 +234,8 @@ def cal_individual_val(component_nozzle, component_point_pos, designated_nozzle,
return V[-1], pickup_result, pickup_cycle_result
def convert_individual_2_result(component_data, component_point_pos, designated_nozzle, pickup_group, pickup_group_cycle,
pair_group, feeder_lane, individual):
def convert_individual_2_result(component_data, component_point_pos, designated_nozzle, pickup_group,
pickup_group_cycle, pair_group, feeder_lane, individual):
component_result, cycle_result, feeder_slot_result = [], [], []
placement_result, head_sequence_result = [], []
@ -418,19 +418,19 @@ def optimizer_hybrid_genetic(pcb_data, component_data, hinter=True):
pick_part = pickup[pickup_index]
# 检查槽位占用情况
if feeder_lane[slot] is not None and pick_part is not None:
if feeder_lane[slot] and pick_part:
assign_available = False
break
# 检查机械限位冲突
if pick_part is not None and (slot - CT_Head[pick_part][0] * interval_ratio <= 0 or
slot + (max_head_index - CT_Head[pick_part][1] - 1) * interval_ratio > max_slot_index // 2):
if pick_part and (slot - CT_Head[pick_part][0] * interval_ratio <= 0 or slot + (
max_head_index - CT_Head[pick_part][1] - 1) * interval_ratio > max_slot_index // 2):
assign_available = False
break
if assign_available:
for idx, component in enumerate(pickup):
if component is not None:
if component:
feeder_lane[assign_slot + idx * interval_ratio] = component
CT_Group_slot[CTIdx] = assign_slot
break
@ -509,32 +509,31 @@ def optimizer_hybrid_genetic(pcb_data, component_data, hinter=True):
with tqdm(total=n_generations) as pbar:
pbar.set_description('hybrid genetic process')
for _ in range(n_generations):
# calculate fitness value
pop_val = []
for pop_idx, individual in enumerate(population):
val, _, _ = cal_individual_val(component_nozzle, component_point_pos, designated_nozzle, pickup_group,
pickup_group_cycle, pair_group, feeder_part_arrange, individual)
pop_val.append(val)
pop_val.append(val) # val is related to assembly time
idx = np.argmin(pop_val)
if len(best_pop_val) == 0 or pop_val[idx] < best_pop_val[-1]:
best_individual = copy.deepcopy(population[idx])
best_pop_val.append(pop_val[idx])
for _ in range(n_generations):
# idx = np.argmin(pop_val)
# if len(best_pop_val) == 0 or pop_val[idx] < best_pop_val[-1]:
# best_individual = copy.deepcopy(population[idx])
# best_pop_val.append(pop_val[idx])
# min-max convert
max_val = 1.5 * max(pop_val)
pop_val = list(map(lambda v: max_val - v, pop_val))
convert_pop_val = list(map(lambda v: max_val - v, pop_val))
# crossover and mutation
c = 0
new_population = []
new_population, new_pop_val = [], []
for pop in range(population_size):
if pop % 2 == 0 and np.random.random() < crossover_rate:
index1, index2 = roulette_wheel_selection(pop_val), -1
index1, index2 = roulette_wheel_selection(convert_pop_val), -1
while True:
index2 = roulette_wheel_selection(pop_val)
index2 = roulette_wheel_selection(convert_pop_val)
if index1 != index2:
break
# 两点交叉算子
@ -552,13 +551,27 @@ def optimizer_hybrid_genetic(pcb_data, component_data, hinter=True):
new_population.append(offspring1)
new_population.append(offspring2)
# selection
top_k_index = get_top_k_value(pop_val, population_size - len(new_population))
val, _, _ = cal_individual_val(component_nozzle, component_point_pos, designated_nozzle,
pickup_group,
pickup_group_cycle, pair_group, feeder_part_arrange, offspring1)
new_pop_val.append(val)
val, _, _ = cal_individual_val(component_nozzle, component_point_pos, designated_nozzle,
pickup_group,
pickup_group_cycle, pair_group, feeder_part_arrange, offspring2)
new_pop_val.append(val)
# generate next generation
top_k_index = get_top_k_value(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)]
return convert_individual_2_result(component_data, component_point_pos, designated_nozzle, pickup_group,
pickup_group_cycle, pair_group, feeder_lane, best_individual)

57
base_optimizer/optimizer_scanbased.py
View File

@ -3,11 +3,11 @@ from base_optimizer.optimizer_common import *
@timer_wrapper
def optimizer_scanbased(component_data, pcb_data, hinter):
def optimizer_genetic_scanning(component_data, pcb_data, hinter):
population_size = 200 # 种群规模
crossover_rate, mutation_rate = .4, .02
n_generation = 5
n_generation = 500
component_points = [0] * len(component_data)
for i in range(len(pcb_data)):
@ -31,50 +31,52 @@ def optimizer_scanbased(component_data, pcb_data, hinter):
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
# todo: 过程写的有问题，暂时不想改
sigma_scaling(pop_val, 1)
with tqdm(total=n_generation) as pbar:
pbar.set_description('hybrid genetic process')
new_pop_val, new_pop_individual = [], []
# min-max convert
max_val = 1.5 * max(pop_val)
convert_pop_val = list(map(lambda v: max_val - v, pop_val))
for _ in range(n_generation):
# 交叉
for pop in range(population_size):
if pop % 2 == 0 and np.random.random() < crossover_rate:
index1, index2 = roulette_wheel_selection(pop_val), -1
index1, index2 = roulette_wheel_selection(convert_pop_val), -1
while True:
index2 = roulette_wheel_selection(pop_val)
index2 = roulette_wheel_selection(convert_pop_val)
if index1 != index2:
break
# 两点交叉算子
offspring1, offspring2 = cycle_crossover(pop_individual[index1], pop_individual[index2])
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring1)
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
pop_individual.append(offspring1)
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring2)
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
pop_individual.append(offspring2)
sigma_scaling(pop_val, 1)
# 变异
if np.random.random() < mutation_rate:
index_ = roulette_wheel_selection(pop_val)
offspring = swap_mutation(pop_individual[index_])
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring)
offspring1 = swap_mutation(offspring1)
pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
pop_individual.append(offspring)
if np.random.random() < mutation_rate:
offspring2 = swap_mutation(offspring2)
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring1)
new_pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
new_pop_individual.append(offspring1)
_, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, offspring2)
new_pop_val.append(feeder_arrange_evaluate(feeder_slot_result, cycle_result))
new_pop_individual.append(offspring2)
# generate next generation
top_k_index = get_top_k_value(pop_val, population_size - len(new_pop_individual), reverse=False)
for index in top_k_index:
new_pop_individual.append(pop_individual[index])
new_pop_val.append(pop_val[index])
pop_individual, pop_val = new_pop_individual, new_pop_val
sigma_scaling(pop_val, 1)
new_population, new_popval = [], []
for index in get_top_k_value(pop_val, population_size):
new_population.append(pop_individual[index])
new_popval.append(pop_val[index])
pop_individual, pop_val = new_population, new_popval
# select the best individual
pop = np.argmin(pop_val)
component_result, cycle_result, feeder_slot_result = convert_individual_2_result(component_points, pop_individual[pop])
@ -98,7 +100,6 @@ def convert_individual_2_result(component_points, pop):
feeder_part[gene], feeder_base_points[gene] = idx, component_points[idx]
# TODO: 暂时未考虑可用吸嘴数的限制
# for _ in range(math.ceil(sum(component_points) / max_head_index)):
while True:
# === 周期内循环 ===
assigned_part = [-1 for _ in range(max_head_index)] # 当前扫描到的头分配元件信息

14
dataloader.py
View File

@ -27,7 +27,7 @@ def load_data(filename: str, default_feeder_limit=1, load_cp_data=True, load_fee
# 注册元件检查
part_feeder_assign = defaultdict(set)
part_col = ["part", "desc", "fdr", "nz", 'camera', 'group', 'feeder-limit']
part_col = ["part", "desc", "fdr", "nz", 'camera', 'group', 'feeder-limit', 'points']
try:
if load_cp_data:
component_data = pd.DataFrame(pd.read_csv(filepath_or_buffer='component.txt', sep='\t', header=None),
@ -40,18 +40,18 @@ def load_data(filename: str, default_feeder_limit=1, load_cp_data=True, load_fee
for _, data in pcb_data.iterrows():
part, nozzle = data.part, data.nz.split(' ')[1]
slot = data['fdr'].split(' ')[0]
if part not in component_data['part'].values:
if not cp_auto_register:
raise Exception("unregistered component: " + component_data['part'].values)
else:
component_data = pd.concat([component_data, pd.DataFrame(
[part, '', 'SM8', nozzle, '飞行相机1', 'CHIP-Rect', default_feeder_limit], index=part_col).T],
[part, '', 'SM8', nozzle, '飞行相机1', 'CHIP-Rect', default_feeder_limit, 0], index=part_col).T],
ignore_index=True)
# warning_info = 'register component ' + part + ' with default feeder type'
# warnings.warn(warning_info, UserWarning)
part_index = component_data[component_data['part'] == part].index.tolist()[0]
part_feeder_assign[part].add(slot)
component_data.loc[part_index]['points'] += 1
if nozzle != 'A' and component_data.loc[part_index]['nz'] != nozzle:
warning_info = 'the nozzle type of component ' + part + ' is not consistent with the pcb data'
@ -64,9 +64,8 @@ def load_data(filename: str, default_feeder_limit=1, load_cp_data=True, load_fee
# 读取供料器基座数据
feeder_data = pd.DataFrame(columns=['slot', 'part', 'arg']) # arg表示是否为预分配，不表示分配数目
if load_feeder_data:
for data in pcb_data.iterrows():
fdr = data[1]['fdr']
slot, part = fdr.split(' ')
for _, data in pcb_data.iterrows():
slot, part = data['fdr'].split(' ')
if slot[0] != 'F' and slot[0] != 'R':
continue
slot = int(slot[1:]) if slot[0] == 'F' else int(slot[1:]) + max_slot_index // 2
@ -80,6 +79,5 @@ def load_data(filename: str, default_feeder_limit=1, load_cp_data=True, load_fee
feeder_data.sort_values(by='slot', ascending=True, inplace=True, ignore_index=True)
# plt.scatter(pcb_data["x"], pcb_data["y"])
# plt.show()
pcb_data = pcb_data.sort_values(by="x", ascending=False)
return pcb_data, component_data, feeder_data

119
optimizer.py
View File

@ -1,24 +1,34 @@
import copy
import math
import matplotlib.pyplot as plt
import pandas as pd
from base_optimizer.optimizer_aggregation import *
from base_optimizer.optimizer_scanbased import *
from base_optimizer.optimizer_celldivision import *
from base_optimizer.optimizer_hybridgenetic import *
from base_optimizer.optimizer_feederpriority import *
from dataloader import *
from optimizer_genetic import *
from optimizer_heuristic import *
def optimizer(pcb_data, component_data, assembly_line_optimizer, single_machine_optimizer):
assignment_result = assemblyline_optimizer_genetic(pcb_data, component_data)
def deviation(data):
assert len(data) > 0
average, variance = sum(data) / len(data), 0
for v in data:
variance += (v - average) ** 2
return variance / len(data)
# assignment_result = [[0, 0, 0, 0, 216, 0, 0], [0, 0, 0, 0, 216, 0, 0], [36, 24, 12, 12, 0, 36, 12]]
def optimizer(pcb_data, component_data, assembly_line_optimizer, single_machine_optimizer):
# todo: 由于吸嘴更换更因素的存在，在处理PCB8数据时，遗传算法因在负载均衡过程中对这一因素进行了考虑，性能更优
# assignment_result = assemblyline_optimizer_heuristic(pcb_data, component_data)
assignment_result = assemblyline_optimizer_genetic(pcb_data, component_data)
print(assignment_result)
assignment_result_cpy = copy.deepcopy(assignment_result)
placement_points, placement_time = [], []
partial_pcb_data, partial_component_data = defaultdict(pd.DataFrame), defaultdict(pd.DataFrame)
for machine_index in range(max_machine_index):
@ -26,7 +36,9 @@ def optimizer(pcb_data, component_data, assembly_line_optimizer, single_machine_
partial_component_data[machine_index] = component_data.copy(deep=True)
placement_points.append(sum(assignment_result[machine_index]))
# averagely assign available feeder
assert sum(placement_points) == len(pcb_data)
# === averagely assign available feeder ===
for part_index, data in component_data.iterrows():
feeder_limit = data['feeder-limit']
feeder_points = [assignment_result[machine_index][part_index] for machine_index in range(max_machine_index)]
@ -49,11 +61,14 @@ def optimizer(pcb_data, component_data, assembly_line_optimizer, single_machine_
partial_component_data[machine_index].loc[part_index]['feeder-limit'] += 1
feeder_limit -= 1
for machine_index in range(max_machine_index):
if feeder_points[machine_index] > 0:
assert partial_component_data[machine_index].loc[part_index]['feeder-limit'] > 0
# === assign placements ===
component_machine_index = [0 for _ in range(len(component_data))]
pcb_data = pcb_data.sort_values(by="x", ascending=False)
for _, data in pcb_data.iterrows():
part = data['part']
part_index = component_data[component_data['part'] == part].index.tolist()[0]
part_index = component_data[component_data['part'] == data['part']].index.tolist()[0]
while True:
machine_index = component_machine_index[part_index]
if assignment_result[machine_index][part_index] == 0:
@ -64,11 +79,60 @@ def optimizer(pcb_data, component_data, assembly_line_optimizer, single_machine_
assignment_result[machine_index][part_index] -= 1
partial_pcb_data[machine_index] = pd.concat([partial_pcb_data[machine_index], pd.DataFrame(data).T])
# === adjust the number of available feeders for single optimization separately ===
for machine_index, data in partial_pcb_data.items():
data = data.reset_index(drop=True)
if len(data) == 0:
continue
part_info = [] # part info list：(part index, part points, available feeder-num, upper feeder-num)
for part_index, cp_data in partial_component_data[machine_index].iterrows():
if assignment_result_cpy[machine_index][part_index]:
part_info.append(
[part_index, assignment_result_cpy[machine_index][part_index], 1, cp_data['feeder-limit']])
part_info = sorted(part_info, key=lambda x: x[1], reverse=True)
start_index, end_index = 0, min(max_head_index - 1, len(part_info) - 1)
while start_index < len(part_info):
assign_part_point, assign_part_index = [], []
for idx_ in range(start_index, end_index + 1):
for _ in range(part_info[idx_][2]):
assign_part_point.append(part_info[idx_][1] / part_info[idx_][2])
assign_part_index.append(idx_)
variance = deviation(assign_part_point)
while start_index != end_index:
part_info_index = assign_part_index[np.argmax(assign_part_point)]
if part_info[part_info_index][2] < part_info[part_info_index][3]: # 供料器数目上限的限制
part_info[part_info_index][2] += 1
end_index -= 1
new_assign_part_point, new_assign_part_index = [], []
for idx_ in range(start_index, end_index + 1):
for _ in range(part_info[idx_][2]):
new_assign_part_point.append(part_info[idx_][1] / part_info[idx_][2])
new_assign_part_index.append(idx_)
new_variance = deviation(new_assign_part_point)
if variance < new_variance:
part_info[part_info_index][2] -= 1
end_index += 1
break
variance = new_variance
assign_part_index, assign_part_point = new_assign_part_index, new_assign_part_point
else:
break
start_index = end_index + 1
end_index = min(start_index + max_head_index - 1, len(part_info) - 1)
# update available feeder number
max_avl_feeder = max(part_info, key=lambda x: x[2])[2]
for info in part_info:
partial_component_data[machine_index].loc[info[0]]['feeder-limit'] = math.ceil(info[2] / max_avl_feeder)
placement_time.append(base_optimizer(machine_index + 1, data, partial_component_data[machine_index],
feeder_data=pd.DataFrame(columns=['slot', 'part', 'arg']),
method=single_machine_optimizer, hinter=True))
@ -86,13 +150,15 @@ def optimizer(pcb_data, component_data, assembly_line_optimizer, single_machine_
# todo: 不同类型元件的组装时间差异
def base_optimizer(machine_index, pcb_data, component_data, feeder_data=None, method='', hinter=False):
if method == 'cell_division': # 基于元胞分裂的遗传算法
component_result, cycle_result, feeder_slot_result = optimizer_celldivision(pcb_data, component_data, False)
component_result, cycle_result, feeder_slot_result = optimizer_celldivision(pcb_data, component_data,
hinter=False)
placement_result, head_sequence = greedy_placement_route_generation(component_data, pcb_data, component_result,
cycle_result, feeder_slot_result)
elif method == 'feeder_priority': # 基于基座扫描的供料器优先算法
elif method == 'feeder_scan': # 基于基座扫描的供料器优先算法
# 第1步：分配供料器位置
nozzle_pattern = feeder_allocate(component_data, pcb_data, feeder_data, False)
nozzle_pattern = feeder_allocate(component_data, pcb_data, feeder_data, figure=False)
# 第2步：扫描供料器基座，确定元件拾取的先后顺序
component_result, cycle_result, feeder_slot_result = feeder_base_scan(component_data, pcb_data, feeder_data,
nozzle_pattern)
@ -105,25 +171,26 @@ def base_optimizer(machine_index, pcb_data, component_data, feeder_data=None, me
elif method == 'hybrid_genetic': # 基于拾取组的混合遗传算法
component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = optimizer_hybrid_genetic(
pcb_data, component_data, False)
pcb_data, component_data, hinter=False)
elif method == 'aggregation': # 基于batch-level的整数规划 + 启发式算法
component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = optimizer_aggregation(
component_data, pcb_data)
elif method == 'scan_based':
component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = optimizer_scanbased(
component_data, pcb_data, False)
elif method == 'genetic_scanning':
component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = optimizer_genetic_scanning(
component_data, pcb_data, hinter=False)
else:
raise 'method is not existed'
if hinter:
optimization_assign_result(component_data, pcb_data, component_result, cycle_result, feeder_slot_result,
nozzle_hinter=False, component_hinter=False, feeder_hinter=False)
nozzle_hinter=True, component_hinter=False, feeder_hinter=False)
print('----- Placement machine ' + str(machine_index) + ' ----- ')
print('-Cycle counter: {}'.format(sum(cycle_result)))
total_nozzle_change_counter, total_pick_counter = 0, 0
total_pick_movement = 0
assigned_nozzle = ['' if idx == -1 else component_data.loc[idx]['nz'] for idx in component_result[0]]
for cycle in range(len(cycle_result)):
@ -141,25 +208,31 @@ def base_optimizer(machine_index, pcb_data, component_data, feeder_data=None, me
pick_slot.add(feeder_slot_result[cycle][head] - head * interval_ratio)
total_pick_counter += len(pick_slot) * cycle_result[cycle]
pick_slot = list(pick_slot)
pick_slot.sort()
for idx in range(len(pick_slot) - 1):
total_pick_movement += abs(pick_slot[idx+1] - pick_slot[idx])
print('-Nozzle change counter: {}'.format(total_nozzle_change_counter))
print('-Pick operation counter: {}'.format(total_pick_counter))
print('-Pick movement: {}'.format(total_pick_movement))
print('------------------------------ ')
# 估算贴装用时
return placement_time_estimate(component_data, pcb_data, component_result, cycle_result, feeder_slot_result,
placement_result, head_sequence, False)
placement_result, head_sequence, hinter=False)
@timer_wrapper
def main():
# warnings.simplefilter('ignore')
# 参数解析
parser = argparse.ArgumentParser(description='assembly line optimizer implementation')
parser.add_argument('--filename', default='PCB1 - FL19-30W.txt', type=str, help='load pcb data')
parser.add_argument('--filename', default='PCB.txt', type=str, help='load pcb data')
parser.add_argument('--auto_register', default=1, type=int, help='register the component according the pcb data')
parser.add_argument('--base_optimizer', default='feeder_priority', type=str,
help='base optimizer for single machine')
parser.add_argument('--assembly_optimizer', default='genetic', type=str, help='optimizer for PCB Assembly Line')
parser.add_argument('--feeder_limit', default=2, type=int,
parser.add_argument('--base_optimizer', default='feeder_scan', type=str, help='base optimizer for single machine')
parser.add_argument('--assembly_optimizer', default='heuristic', type=str, help='optimizer for PCB Assembly Line')
parser.add_argument('--feeder_limit', default=1, type=int,
help='the upper feeder limit for each type of component')
params = parser.parse_args()

11
optimizer_genetic.py
View File

@ -1,3 +1,4 @@
# implementation of <<An integrated allocation method for the PCB assembly line balancing problem with nozzle changes>>
import matplotlib.pyplot as plt
from base_optimizer.optimizer_common import *
@ -5,10 +6,9 @@ from base_optimizer.optimizer_common import *
def selective_initialization(component_points, component_feeders, population_size):
population = [] # population initialization
for _ in range(population_size):
individual = []
for part_index, points in component_points.items():
for part_index, points in component_points:
if points == 0:
continue
# 可用机器数
@ -50,7 +50,7 @@ def selective_crossover(component_points, component_feeders, mother, father, non
one_counter, feasible_cut_line = 0, []
idx = 0
for part_index, points in component_points.items():
for part_index, points in component_points:
one_counter = 0
idx_, mother_cut_line, father_cut_line = 0, [-1], [-1]
@ -136,7 +136,7 @@ def cal_individual_val(component_points, component_nozzle, individual):
machine_component_points = [[] for _ in range(max_machine_index)]
# decode the component allocation
for points in component_points.values():
for _, points in component_points:
component_gene = individual[idx: idx + points + max_machine_index - 1]
machine_idx, component_counter = 0, 0
for gene in component_gene:
@ -206,6 +206,7 @@ def assemblyline_optimizer_genetic(pcb_data, component_data):
# crossover rate & mutation rate: 80% & 10%
# population size: 200
# the number of generation: 500
np.random.seed(0)
crossover_rate, mutation_rate = 0.8, 0.1
population_size, n_generations = 200, 500
@ -219,6 +220,8 @@ def assemblyline_optimizer_genetic(pcb_data, component_data):
component_feeders[part_index] = component_data.loc[part_index]['feeder-limit']
component_nozzle[part_index] = nozzle
component_points = sorted(component_points.items(), key=lambda x: x[0]) # 决定染色体排列顺序
# population initialization
best_popval = []
population = selective_initialization(component_points, component_feeders, population_size)

142
optimizer_heuristic.py
View File

@ -1,16 +1,146 @@
import math
import numpy as np
from base_optimizer.optimizer_common import *
# TODO: 需要考虑贴装点分布位置的限制
def assembly_time_estimator(pcb_data, component_data, assignment):
return 0
# TODO: consider with the PCB placement topology
def assembly_time_estimator(component_points, component_feeders, component_nozzle, assignment_points):
# todo: how to deal with nozzle change
n_cycle, n_nz_change, n_gang_pick = 0, 0, 0
nozzle_heads, nozzle_points = defaultdict(int), defaultdict(int)
for idx, points in enumerate(assignment_points):
if points == 0:
continue
nozzle_points[component_nozzle[idx]] += points
nozzle_heads[component_nozzle[idx]] = 1
while sum(nozzle_heads.values()) != max_head_index:
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
n_cycle = max(map(lambda x: math.ceil(nozzle_points[x[0]] / x[1]), nozzle_heads.items()))
# calculate the number of simultaneous pickup
head_index, nozzle_cycle = 0, [[] for _ in range(max_head_index)]
for nozzle, heads in nozzle_heads.items():
head_index_cpy, points = head_index, nozzle_points[nozzle]
for _ in range(heads):
nozzle_cycle[head_index].append([nozzle, points // heads])
head_index += 1
points %= heads
while points:
nozzle_cycle[head_index_cpy][1] += 1
points -= 1
head_index_cpy += 1
# nozzle_cycle_index = [0 for _ in range(max_head_index)]
return n_cycle, n_nz_change, n_gang_pick
def assemblyline_optimizer_heuristic(pcb_data, component_data):
assignment_result = []
# the number of placement points, the number of available feeders, and nozzle type of component respectively
component_number = len(component_data)
component_points = [0 for _ in range(component_number)]
component_feeders = [0 for _ in range(component_number)]
component_nozzle = [0 for _ in range(component_number)]
component_part = [0 for _ in range(component_number)]
# for machine_index in range(max_machine_index):
# assembly_time_estimator(pcb_data, component_data, assignment_result[machine_index])
nozzle_points = defaultdict(int) # the number of placements of nozzle
for _, data in pcb_data.iterrows():
part_index = component_data[component_data['part'] == data['part']].index.tolist()[0]
nozzle = component_data.loc[part_index]['nz']
component_points[part_index] += 1
component_feeders[part_index] = component_data.loc[part_index]['feeder-limit']
# component_feeders[part_index] = math.ceil(component_data.loc[part_index]['feeder-limit'] / max_feeder_limit)
component_nozzle[part_index] = nozzle
component_part[part_index] = data['part']
nozzle_points[nozzle] += 1
# first step: generate the initial solution with equalized workload
assignment_result = [[0 for _ in range(len(component_points))] for _ in range(max_machine_index)]
assignment_points = [0 for _ in range(max_machine_index)]
weighted_points = list(
map(lambda x: x[1] + 1e-5 * nozzle_points[component_nozzle[x[0]]], enumerate(component_points)))
for part_index in np.argsort(weighted_points):
if (total_points := component_points[part_index]) == 0: # total placements for each component type
continue
machine_set = []
# define the machine that assigning placement points (considering the feeder limitation)
for machine_index in np.argsort(assignment_points):
if len(machine_set) >= component_points[part_index] or len(machine_set) >= component_feeders[part_index]:
break
machine_set.append(machine_index)
# Allocation of mounting points to available machines according to the principle of equality
while total_points:
assign_machine = list(filter(lambda x: assignment_points[x] == min(assignment_points), machine_set))
if len(assign_machine) == len(machine_set):
# averagely assign point to all available machines
points = total_points // len(assign_machine)
for machine_index in machine_set:
assignment_points[machine_index] += points
assignment_result[machine_index][part_index] += points
total_points -= points * len(assign_machine)
for machine_index in machine_set:
if total_points == 0:
break
assignment_points[machine_index] += 1
assignment_result[machine_index][part_index] += 1
total_points -= 1
else:
# assigning placements to make up for the gap between the least and the second least
second_least_machine, second_least_machine_points = -1, max(assignment_points) + 1
for idx in machine_set:
if assignment_points[idx] < second_least_machine_points and assignment_points[idx] != min(
assignment_points):
second_least_machine_points = assignment_points[idx]
second_least_machine = idx
assert second_least_machine != -1
if len(assign_machine) * (second_least_machine_points - min(assignment_points)) < total_points:
min_points = min(assignment_points)
total_points -= len(assign_machine) * (second_least_machine_points - min_points)
for machine_index in assign_machine:
assignment_points[machine_index] += (second_least_machine_points - min_points)
assignment_result[machine_index][part_index] += (
second_least_machine_points - min_points)
else:
points = total_points // len(assign_machine)
for machine_index in assign_machine:
assignment_points[machine_index] += points
assignment_result[machine_index][part_index] += points
total_points -= points * len(assign_machine)
for machine_index in assign_machine:
if total_points == 0:
break
assignment_points[machine_index] += 1
assignment_result[machine_index][part_index] += 1
total_points -= 1
# todo: implementation
# second step: estimate the assembly time for each machine
# third step: adjust the assignment results to reduce maximal assembly time among all machines
return assignment_result

58
result_analysis.py
View File

@ -362,14 +362,24 @@ def optimization_assign_result(component_data, pcb_data, component_result, cycle
nozzle_assign = pd.DataFrame(columns=columns)
for cycle, components in enumerate(component_result):
nozzle_assign.loc[cycle, 'cycle'] = cycle_result[cycle]
nozzle_assign_row = len(nozzle_assign)
nozzle_assign.loc[nozzle_assign_row, 'cycle'] = cycle_result[cycle]
for head in range(max_head_index):
index = component_result[cycle][head]
if index == -1:
nozzle_assign.loc[cycle, 'H{}'.format(head + 1)] = ''
nozzle_assign.loc[nozzle_assign_row, 'H{}'.format(head + 1)] = ''
else:
nozzle = component_data.loc[index]['nz']
nozzle_assign.loc[cycle, 'H{}'.format(head + 1)] = nozzle
nozzle_assign.loc[nozzle_assign_row, 'H{}'.format(head + 1)] = nozzle
for head in range(max_head_index):
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('')
@ -449,11 +459,7 @@ def placement_time_estimate(component_data, pcb_data, component_result, cycle_re
warnings.warn(info, UserWarning)
return 0.
t_pick, t_place = .078, .051 # 贴装/拾取用时
t_nozzle_put, t_nozzle_pick = 0.9, 0.75 # 装卸吸嘴用时
t_fix_camera_check = 0.12 # 固定相机检测时间
total_moving_time = .0 # 总移动用时
total_pickup_time, total_round_time, total_place_time = .0, .0, 0 # 拾取用时、往返用时、贴装用时
total_operation_time = .0 # 操作用时
total_nozzle_change_counter = 0 # 总吸嘴更换次数
total_pick_counter = 0 # 总拾取次数
@ -492,8 +498,10 @@ def placement_time_estimate(component_data, pcb_data, component_result, cycle_re
# ANC处进行吸嘴更换
if nozzle_pick_counter + nozzle_put_counter > 0:
next_pos = anc_marker_pos
total_moving_time += max(axis_moving_time(cur_pos[0] - next_pos[0], 0),
move_time = max(axis_moving_time(cur_pos[0] - next_pos[0], 0),
axis_moving_time(cur_pos[1] - next_pos[1], 1))
total_round_time += move_time
total_distance += max(abs(cur_pos[0] - next_pos[0]), abs(cur_pos[1] - next_pos[1]))
cur_pos = next_pos
@ -501,15 +509,21 @@ def placement_time_estimate(component_data, pcb_data, component_result, cycle_re
pick_slot = sorted(pick_slot, reverse=True)
# 拾取路径(自右向左)
for slot in pick_slot:
for idx, slot in enumerate(pick_slot):
if slot < max_slot_index // 2:
next_pos = [slotf1_pos[0] + slot_interval * (slot - 1), slotf1_pos[1]]
else:
next_pos = [slotr1_pos[0] - slot_interval * (max_slot_index - slot - 1), slotr1_pos[1]]
total_operation_time += t_pick
total_pick_counter += 1
total_moving_time += max(axis_moving_time(cur_pos[0] - next_pos[0], 0),
move_time = max(axis_moving_time(cur_pos[0] - next_pos[0], 0),
axis_moving_time(cur_pos[1] - next_pos[1], 1))
if idx == 0:
total_round_time += move_time
else:
total_pickup_time += move_time
total_distance += max(abs(cur_pos[0] - next_pos[0]), abs(cur_pos[1] - next_pos[1]))
if slot != pick_slot[0]:
total_pick_distance += max(abs(cur_pos[0] - next_pos[0]), abs(cur_pos[1] - next_pos[1]))
@ -522,8 +536,10 @@ def placement_time_estimate(component_data, pcb_data, component_result, cycle_re
camera = component_data.loc[component_result[cycle_set][head]]['camera']
if camera == '固定相机':
next_pos = [fix_camera_pos[0] - head * head_interval, fix_camera_pos[1]]
total_moving_time += max(axis_moving_time(cur_pos[0] - next_pos[0], 0),
move_time = max(axis_moving_time(cur_pos[0] - next_pos[0], 0),
axis_moving_time(cur_pos[1] - next_pos[1], 1))
total_round_time += move_time
total_distance += max(abs(cur_pos[0] - next_pos[0]), abs(cur_pos[1] - next_pos[1]))
total_operation_time += t_fix_camera_check
cur_pos = next_pos
@ -545,22 +561,26 @@ def placement_time_estimate(component_data, pcb_data, component_result, cycle_re
# 考虑R轴预旋转，补偿同轴角度转动带来的额外贴装用时
total_operation_time += head_rotary_time(mount_angle[0]) # 补偿角度转动带来的额外贴装用时
total_operation_time += t_nozzle_put * nozzle_put_counter + t_nozzle_pick * nozzle_pick_counter
for pos in mount_pos:
for idx, pos in enumerate(mount_pos):
total_operation_time += t_place
total_moving_time += max(axis_moving_time(cur_pos[0] - pos[0], 0),
axis_moving_time(cur_pos[1] - pos[1], 1))
move_time = max(axis_moving_time(cur_pos[0] - pos[0], 0), axis_moving_time(cur_pos[1] - pos[1], 1))
if idx == 0:
total_round_time += move_time
else:
total_place_time += move_time
total_distance += max(abs(cur_pos[0] - pos[0]), abs(cur_pos[1] - pos[1]))
cur_pos = pos
total_nozzle_change_counter += nozzle_put_counter + nozzle_pick_counter
total_time = total_moving_time + total_operation_time
total_time = total_pickup_time + total_round_time + total_place_time + total_operation_time
minutes, seconds = int(total_time // 60), int(total_time) % 60
millisecond = int((total_time - minutes * 60 - seconds) * 60)
if hinter:
optimization_assign_result(component_data, pcb_data, component_result, cycle_result, feeder_slot_result,
nozzle_hinter=True, component_hinter=True, feeder_hinter=True)
nozzle_hinter=False, component_hinter=False, feeder_hinter=False)
print('-Cycle counter: {}'.format(sum(cycle_result)))
print('-Nozzle change counter: {}'.format(total_nozzle_change_counter // 2))
@ -570,7 +590,9 @@ def placement_time_estimate(component_data, pcb_data, component_result, cycle_re
print('-Expected picking tour length: {} mm'.format(total_pick_distance))
print('-Expected total tour length: {} mm'.format(total_distance))
print('-Expected total moving time: {} s'.format(total_moving_time))
print('-Expected total moving time: {} s with pick: {}, round: {}, place = {}'.format(
total_pickup_time + total_round_time + total_place_time, total_pickup_time, total_round_time,
total_place_time))
print('-Expected total operation time: {} s'.format(total_operation_time))
if minutes > 0: