调整工程架构，增补了几种算法，初步添加神经网路训练拟合代码
This commit is contained in:
855
base_optimizer/optimizer_twophase.py
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855
base_optimizer/optimizer_twophase.py
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from base_optimizer.optimizer_common import *
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def list_range(start, end=None):
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return list(range(start)) if end is None else list(range(start, end))
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@timer_wrapper
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def gurobi_optimizer(pcb_data, component_data, feeder_data, reduction=True, partition=True, initial=False, hinter=True):
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# data preparation: convert data to index
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component_list, nozzle_list = defaultdict(int), defaultdict(int)
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cpidx_2_part, nzidx_2_nozzle, cpidx_2_nzidx = {}, {}, {}
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arg_slot_rng = None if len(feeder_data) == 0 else [feeder_data.iloc[0].slot, feeder_data.iloc[-1].slot]
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for idx, data in component_data.iterrows():
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part, nozzle = data.part, data.nz
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cpidx_2_part[idx] = part
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nz_key = [key for key, val in nzidx_2_nozzle.items() if val == nozzle]
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nz_idx = len(nzidx_2_nozzle) if len(nz_key) == 0 else nz_key[0]
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nzidx_2_nozzle[nz_idx] = nozzle
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component_list[part] = 0
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cpidx_2_nzidx[idx] = nz_idx
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for _, data in pcb_data.iterrows():
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idx = component_data[component_data.part == data.part].index.tolist()[0]
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nozzle = component_data.loc[idx].nz
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nozzle_list[nozzle] += 1
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component_list[data.part] += 1
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part_feederbase = defaultdict(int)
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if feeder_data is not None:
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for _, data in feeder_data.iterrows():
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idx = -1
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for idx, part_ in cpidx_2_part.items():
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if data.part == part_:
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break
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assert idx != -1
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part_feederbase[idx] = data.slot # part index - slot
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if not reduction:
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ratio = 2 # 直接导入飞达数据时，采用正常吸杆间隔
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else:
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if len(component_list) <= 1.5 * max_head_index:
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ratio = 1
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else:
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ratio = 2
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I, J = len(cpidx_2_part.keys()), len(nzidx_2_nozzle.keys())
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# === determine the hyper-parameter of L ===
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# first phase: calculate the number of heads for each type of nozzle
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nozzle_heads = defaultdict(int)
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for nozzle in nozzle_list.keys():
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nozzle_heads[nozzle] = 1
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while sum(nozzle_heads.values()) != max_head_index:
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max_cycle_nozzle = None
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for nozzle, head_num in nozzle_heads.items():
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if max_cycle_nozzle is None or nozzle_list[nozzle] / head_num > nozzle_list[max_cycle_nozzle] / \
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nozzle_heads[max_cycle_nozzle]:
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max_cycle_nozzle = nozzle
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assert max_cycle_nozzle is not None
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nozzle_heads[max_cycle_nozzle] += 1
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nozzle_comp_points = defaultdict(list)
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for part, points in component_list.items():
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idx = component_data[component_data.part == part].index.tolist()[0]
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nozzle = component_data.loc[idx].nz
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nozzle_comp_points[nozzle].append([part, points])
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level = 1 if len(component_list) == 1 or len(component_list) % max_head_index == 0 else 2
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part_assignment, cycle_assignment = [], []
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def aux_func(info):
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return max(map(lambda points: max([p[1] for p in points]), info))
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pre_objbst, pre_changetime = None, None
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def terminate_condition(mdl, where):
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if where == GRB.Callback.MIP:
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objbst = mdl.cbGet(GRB.Callback.MIP_OBJBST)
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changetime = mdl.cbGet(GRB.Callback.RUNTIME)
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nonlocal pre_objbst, pre_changetime
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# condition: value change
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if pre_objbst and abs(pre_objbst - objbst) < 1e-3:
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if pre_changetime and changetime - pre_changetime > 45:
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# pass
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mdl.terminate()
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else:
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pre_changetime = changetime
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pre_objbst = objbst
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def recursive_assign(assign_points, nozzle_compo_points, cur_level, total_level) -> int:
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def func(points):
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return map(lambda points: max([p[1] for p in points]), points)
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if cur_level > total_level and sum(func(nozzle_compo_points.values())) == 0:
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return 0
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elif assign_points <= 0 and cur_level == 1:
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return -1 # backtrack
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elif assign_points <= 0 or cur_level > total_level:
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return 1 # fail
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nozzle_compo_points_cpy = copy.deepcopy(nozzle_compo_points)
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prev_assign = 0
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for part in part_assignment[cur_level - 1]:
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if part != -1:
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prev_assign += 1
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head_idx = 0
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for nozzle, head in nozzle_heads.items():
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while head:
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min_idx = -1
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for idx, (part, points) in enumerate(nozzle_compo_points_cpy[nozzle]):
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if points >= assign_points and (
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min_idx == -1 or points < nozzle_compo_points_cpy[nozzle][min_idx][1]):
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min_idx = idx
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part_assignment[cur_level - 1][head_idx] = -1 if min_idx == -1 else \
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nozzle_compo_points_cpy[nozzle][min_idx][0]
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if min_idx != -1:
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nozzle_compo_points_cpy[nozzle][min_idx][1] -= assign_points
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head -= 1
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head_idx += 1
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cycle_assignment[cur_level - 1] = assign_points
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for part in part_assignment[cur_level - 1]:
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if part != -1:
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prev_assign -= 1
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if prev_assign == 0:
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res = 1
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else:
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points = min(len(pcb_data) // max_head_index + 1, aux_func(nozzle_compo_points_cpy.values()))
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res = recursive_assign(points, nozzle_compo_points_cpy, cur_level + 1, total_level)
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if res == 0:
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return 0
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elif res == 1:
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# All cycles have been completed, but there are still points left to be allocated
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return recursive_assign(assign_points - 1, nozzle_compo_points, cur_level, total_level)
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# second phase: (greedy) recursive search to assign points for each cycle set and obtain an initial solution
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while True:
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part_assignment = [[-1 for _ in range(max_head_index)] for _ in range(level)]
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cycle_assignment = [-1 for _ in range(level)]
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points = min(len(pcb_data) // max_head_index + 1, max(component_list.values()))
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if recursive_assign(points, nozzle_comp_points, 1, level) == 0:
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break
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level += 1
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weight_cycle, weight_nz_change, weight_pick = 2, 3, 2
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L = len(cycle_assignment) if partition else len(pcb_data)
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S = ratio * I if len(feeder_data) == 0 else arg_slot_rng[-1] - arg_slot_rng[0] + 1 # the available feeder num
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M = len(pcb_data) # a sufficiently large number (number of placement points)
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HC = [[0 for _ in range(J)] for _ in range(I)]
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for i in range(I):
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for j in range(J):
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HC[i][j] = 1 if cpidx_2_nzidx[i] == j else 0
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mdl = Model('SMT')
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mdl.setParam('Seed', 0)
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mdl.setParam('OutputFlag', hinter) # set whether output the debug information
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mdl.setParam('TimeLimit', 3600 * 3)
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mdl.setParam('PoolSearchMode', 2)
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mdl.setParam('PoolSolutions', 3e2)
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mdl.setParam('PoolGap', 1e-4)
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# mdl.setParam('MIPFocus', 2)
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# mdl.setParam("Heuristics", 0.5)
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# Use only if other methods, including exploring the tree with the default settings, do not yield a viable solution
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# mdl.setParam("ZeroObjNodes", 100)
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# === Decision Variables ===
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x = mdl.addVars(list_range(I), list_range(S), list_range(max_head_index), list_range(L), vtype=GRB.BINARY, name='x')
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y = mdl.addVars(list_range(I), list_range(max_head_index), list_range(L), vtype=GRB.BINARY, name='y')
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v = mdl.addVars(list_range(S), list_range(max_head_index), list_range(L), vtype=GRB.BINARY, name='v')
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c = mdl.addVars(list_range(I), list_range(max_head_index), list_range(L), vtype=GRB.INTEGER, name='c')
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mdl.addConstrs(
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c[i, h, l] <= component_list[cpidx_2_part[i]] for i in range(I) for h in range(max_head_index) for l in
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range(L))
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# todo: the condition for upper limits of feeders exceed 1
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f = {}
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for i in range(I):
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if i not in part_feederbase.keys():
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for s in range(S):
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f[s, i] = mdl.addVar(vtype=GRB.BINARY, name='f_' + str(s) + '_' + str(i))
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else:
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for s in range(S):
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f[s, i] = 1 if part_feederbase[i] == s + arg_slot_rng[0] else 0
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p = mdl.addVars(list_range(-(max_head_index - 1) * ratio, S), list_range(L), vtype=GRB.BINARY, name='p')
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z = mdl.addVars(list_range(J), list_range(max_head_index), list_range(L), vtype=GRB.BINARY)
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d = mdl.addVars(list_range(L), list_range(max_head_index), vtype=GRB.INTEGER, name='d')
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d_plus = mdl.addVars(list_range(J), list_range(max_head_index), list_range(L), vtype=GRB.INTEGER,
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name='d_plus')
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d_minus = mdl.addVars(list_range(J), list_range(max_head_index), list_range(L), vtype=GRB.INTEGER,
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name='d_minus')
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max_cycle = math.ceil(len(pcb_data) / max_head_index)
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PU = mdl.addVars(list_range(-(max_head_index - 1) * ratio, S), list_range(L), vtype=GRB.INTEGER, name='PU')
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WL = mdl.addVars(list_range(L), vtype=GRB.INTEGER, ub=max_cycle, name='WL')
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NC = mdl.addVars(list_range(max_head_index), vtype=GRB.INTEGER, name='NC')
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part_2_cpidx = defaultdict(int)
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for idx, part in cpidx_2_part.items():
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part_2_cpidx[part] = idx
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if initial:
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# initial some variables to speed up the search process
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# ensure the priority of the workload assignment
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cycle_index = sorted(range(len(cycle_assignment)), key=lambda k: cycle_assignment[k], reverse=True)
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part_list = []
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for cycle in cycle_index:
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cycle_part = part_assignment[cycle]
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for part in cycle_part:
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if part != -1 and part not in part_list:
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part_list.append(part)
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slot = 0
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for part in part_list:
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if feeder_data is not None:
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while slot in feeder_data.keys():
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slot += 1 # skip assigned feeder slot
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if part_2_cpidx[part] in part_feederbase.keys():
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continue
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part_feederbase[part_2_cpidx[part]] = slot
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# f[slot, part_2_cpidx[part]].Start = 1
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slot += 1
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for idx, cycle in enumerate(cycle_index):
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WL[idx].Start = cycle_assignment[cycle]
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for h in range(max_head_index):
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part = part_assignment[cycle][h]
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if part == -1:
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continue
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i = part_2_cpidx[part]
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y[i, h, idx].Start = 1
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v[part_feederbase[i], h, idx].Start = 1
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# === Objective ===
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mdl.setObjective(weight_cycle * quicksum(WL[l] for l in range(L)) + weight_nz_change * quicksum(
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NC[h] for h in range(max_head_index)) + weight_pick * quicksum(
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PU[s, l] for s in range(-(max_head_index - 1) * ratio, S) for l in range(L)))
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# === Constraint ===
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if not partition:
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mdl.addConstrs(WL[l] <= 1 for l in range(L))
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# work completion
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# mdl.addConstrs(c[i, h, l] == WL[l] * y[i, h, l] for i in range(I) for h in range(max_head_index) for l in range(L))
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mdl.addConstrs(
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c[i, h, l] <= max_cycle * y[i, h, l] for i in range(I) for h in range(max_head_index) for l in range(L))
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mdl.addConstrs(c[i, h, l] <= WL[l] for i in range(I) for h in range(max_head_index) for l in range(L))
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mdl.addConstrs(
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c[i, h, l] >= WL[l] - max_cycle * (1 - y[i, h, l]) for i in range(I) for h in range(max_head_index) for l in
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range(L))
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mdl.addConstrs(
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quicksum(c[i, h, l] for h in range(max_head_index) for l in range(L)) == component_list[cpidx_2_part[i]] for i
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in range(I))
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# variable constraint
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mdl.addConstrs(quicksum(y[i, h, l] for i in range(I)) <= 1 for h in range(max_head_index) for l in range(L))
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# simultaneous pick
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for s in range(-(max_head_index - 1) * ratio, S):
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rng = list(range(max(0, -math.floor(s / ratio)), min(max_head_index, math.ceil((S - s) / ratio))))
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for l in range(L):
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mdl.addConstr(quicksum(v[s + h * ratio, h, l] for h in rng) <= max_head_index * p[s, l])
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mdl.addConstr(quicksum(v[s + h * ratio, h, l] for h in rng) >= p[s, l])
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# mdl.addConstrs(PU[s, l] == p[s, l] * WL[l] for s in range(-(max_head_index - 1) * ratio, S) for l in range(L))
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mdl.addConstrs(PU[s, l] <= max_cycle * p[s, l] for s in range(-(max_head_index - 1) * ratio, S) for l in range(L))
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mdl.addConstrs(PU[s, l] <= WL[l] for s in range(-(max_head_index - 1) * ratio, S) for l in range(L))
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mdl.addConstrs(
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PU[s, l] >= WL[l] - max_cycle * (1 - p[s, l]) for s in range(-(max_head_index - 1) * ratio, S) for l in
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range(L))
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# nozzle change
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mdl.addConstrs(
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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
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in range(max_head_index))
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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
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in range(max_head_index))
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mdl.addConstrs(
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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
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in range(L - 1) for h in range(max_head_index))
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mdl.addConstrs(2 * d[L - 1, h] == quicksum(d_plus[j, h, L - 1] for j in range(J)) + quicksum(
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d_minus[j, h, L - 1] for j in range(J)) for h in range(max_head_index))
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mdl.addConstrs(NC[h] == quicksum(d[l, h] for l in range(L)) for h in range(max_head_index))
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mdl.addConstrs(quicksum(y[i, h, l] for i in range(I) for h in range(max_head_index)) * M >= WL[l] for l in range(L))
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# nozzle-component compatibility
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mdl.addConstrs(
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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(max_head_index)
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for l in range(L))
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# available number of feeder
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mdl.addConstrs(quicksum(f[s, i] for s in range(S)) <= 1 for i in range(I))
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# available number of nozzle
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mdl.addConstrs(quicksum(z[j, h, l] for h in range(max_head_index)) <= max_head_index for j in range(J) for l in range(L))
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# upper limit for occupation for feeder slot
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mdl.addConstrs(quicksum(f[s, i] for i in range(I)) <= 1 for s in range(S))
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mdl.addConstrs(
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quicksum(v[s, h, l] for s in range(S)) >= quicksum(y[i, h, l] for i in range(I)) for h in range(max_head_index)
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for l in range(L))
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# others
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mdl.addConstrs(quicksum(z[j, h, l] for j in range(J)) <= 1 for h in range(max_head_index) for l in range(L))
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# mdl.addConstrs(
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# quicksum(x[i, s, h, l] for h in range(max_head_index) for l in range(L)) >= f[s, i] for i in range(I)
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# for s in range(S))
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# mdl.addConstrs(
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# quicksum(x[i, s, h, l] for h in range(max_head_index) for l in range(L)) <= M * f[s, i] for i in
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# range(I) for s in range(S))
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mdl.addConstrs(
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f[s, i] >= x[i, s, h, l] for s in range(S) for i in range(I) for h in range(max_head_index) for l in range(L))
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mdl.addConstrs(
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quicksum(x[i, s, h, l] for h in range(max_head_index) for l in range(L)) >= f[s, i] for s in
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range(S) for i in range(I))
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# the constraints to speed up the search process
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mdl.addConstrs(
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quicksum(x[i, s, h, l] for i in range(I) for s in range(S)) <= 1 for h in range(max_head_index) for l
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in range(L))
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if reduction:
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mdl.addConstrs(WL[l] >= WL[l + 1] for l in range(L - 1))
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# mdl.addConstr(quicksum(WL[l] for l in range(L)) <= sum(cycle_assignment))
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mdl.addConstr(quicksum(WL[l] for l in range(L)) >= math.ceil(len(pcb_data) / max_head_index))
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# mdl.addConstrs(WL[l] >= WL[l + 1] for l in range(L - 1))
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# mdl.addConstrs(quicksum(z[j, h, l] for j in range(J) for h in range(max_head_index)) >= quicksum(
|
||||
# z[j, h, l + 1] for j in range(J) for h in range(max_head_index)) for l in range(L - 1))
|
||||
#
|
||||
mdl.addConstrs(y[i, h, l] <= WL[l] for i in range(I) for h in range(max_head_index) for l in range(L))
|
||||
mdl.addConstrs(v[s, h, l] <= WL[l] for s in range(S) for h in range(max_head_index) 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(max_head_index)
|
||||
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(max_head_index)
|
||||
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(max_head_index)
|
||||
for l in range(L))
|
||||
|
||||
# === search process ===
|
||||
mdl.update()
|
||||
# mdl.write('mdl.lp')
|
||||
if hinter:
|
||||
print('num of constrs: ', str(len(mdl.getConstrs())), ', num of vars: ', str(len(mdl.getVars())))
|
||||
|
||||
mdl.optimize(terminate_condition)
|
||||
|
||||
# === result generation ===
|
||||
nozzle_assign, component_assign = [], []
|
||||
feeder_assign, cycle_assign = [], []
|
||||
if mdl.Status == GRB.OPTIMAL or mdl.Status == GRB.INTERRUPTED or mdl.Status == GRB.TIME_LIMIT:
|
||||
# === selection from solution pool ===
|
||||
component_pos, component_avg_pos = defaultdict(list), defaultdict(list)
|
||||
for _, data in pcb_data.iterrows():
|
||||
component_index = component_data[component_data.part == data.part].index.tolist()[0]
|
||||
component_pos[component_index].append([data.x, data.y])
|
||||
|
||||
for i in component_pos.keys():
|
||||
component_pos[i] = sorted(component_pos[i], key=lambda pos: (pos[0], pos[1]))
|
||||
component_avg_pos[i] = [sum(map(lambda pos: pos[0], component_pos[i])) / len(component_pos[i]),
|
||||
sum(map(lambda pos: pos[1], component_pos[i])) / len(component_pos[i])]
|
||||
|
||||
min_dist, solution_number = None, -1
|
||||
for sol_counter in range(mdl.SolCount):
|
||||
nozzle_assign, component_assign = [], []
|
||||
feeder_assign, cycle_assign = [], []
|
||||
|
||||
mdl.Params.SolutionNumber = sol_counter
|
||||
pos_counter = defaultdict(int)
|
||||
|
||||
dist = 0
|
||||
cycle_placement, cycle_points = defaultdict(list), defaultdict(list)
|
||||
for l in range(L):
|
||||
if abs(WL[l].Xn) <= 1e-4:
|
||||
continue
|
||||
cycle_placement[l], cycle_points[l] = [-1] * max_head_index, [None] * max_head_index
|
||||
|
||||
for h in range(max_head_index):
|
||||
for l in range(L):
|
||||
if abs(WL[l].Xn) <= 1e-4:
|
||||
continue
|
||||
|
||||
pos_list = []
|
||||
for i in range(I):
|
||||
if abs(y[i, h, l].Xn) <= 1e-4:
|
||||
continue
|
||||
|
||||
for _ in range(round(WL[l].Xn)):
|
||||
pos_list.append(component_pos[i][pos_counter[i]])
|
||||
pos_counter[i] += 1
|
||||
|
||||
cycle_placement[l][h] = i
|
||||
cycle_points[l][h] = [sum(map(lambda pos: pos[0], pos_list)) / len(pos_list),
|
||||
sum(map(lambda pos: pos[1], pos_list)) / len(pos_list)]
|
||||
for l in range(L):
|
||||
if abs(WL[l].Xn) <= 1e-4:
|
||||
continue
|
||||
|
||||
if min_dist is None or dist < min_dist:
|
||||
min_dist = dist
|
||||
solution_number = sol_counter
|
||||
|
||||
mdl.Params.SolutionNumber = solution_number
|
||||
# === 更新吸嘴、元件、周期数优化结果 ===
|
||||
for l in range(L):
|
||||
nozzle_assign.append([-1 for _ in range(max_head_index)])
|
||||
component_assign.append([-1 for _ in range(max_head_index)])
|
||||
feeder_assign.append([-1 for _ in range(max_head_index)])
|
||||
|
||||
cycle_assign.append(round(WL[l].Xn))
|
||||
if abs(WL[l].Xn) <= 1e-4:
|
||||
continue
|
||||
|
||||
for h in range(max_head_index):
|
||||
for i in range(I):
|
||||
if abs(y[i, h, l].Xn - 1) < 1e-4:
|
||||
component_assign[-1][h] = i
|
||||
|
||||
for j in range(J):
|
||||
if HC[i][j]:
|
||||
nozzle_assign[-1][h] = j
|
||||
for s in range(S):
|
||||
if abs(v[s, h, l].Xn - 1) < 1e-4 and component_assign[l][h] != -1:
|
||||
feeder_assign[l][h] = s
|
||||
|
||||
# === 更新供料器分配结果 ==
|
||||
component_head = defaultdict(int)
|
||||
for i in range(I):
|
||||
cycle_num = 0
|
||||
for l, component_cycle in enumerate(component_assign):
|
||||
for head, component in enumerate(component_cycle):
|
||||
if component == i:
|
||||
component_head[i] += cycle_assign[l] * head
|
||||
cycle_num += cycle_assign[l]
|
||||
component_head[i] /= cycle_num # 不同元件的加权拾取贴装头
|
||||
|
||||
average_pos = 0
|
||||
for _, data in pcb_data.iterrows():
|
||||
average_pos += (data.x - component_head[part_2_cpidx[data.part]] * head_interval)
|
||||
|
||||
average_pos /= len(pcb_data) # 实际贴装位置的加权平均
|
||||
average_slot = 0
|
||||
for l in range(L):
|
||||
if abs(WL[l].Xn) <= 1e-4:
|
||||
continue
|
||||
min_slot, max_slot = None, None
|
||||
for head in range(max_head_index):
|
||||
if abs(WL[l].Xn) <= 1e-4 or feeder_assign[l][head] == -1:
|
||||
continue
|
||||
slot = feeder_assign[l][head] - head * ratio
|
||||
if min_slot is None or slot < min_slot:
|
||||
min_slot = slot
|
||||
if max_slot is None or slot > max_slot:
|
||||
max_slot = slot
|
||||
average_slot += (max_slot - min_slot) * cycle_assign[l]
|
||||
average_slot /= sum(cycle_assign)
|
||||
start_slot = round((average_pos + stopper_pos[0] - slotf1_pos[0]) / slot_interval + average_slot / 2) + 1
|
||||
|
||||
for l in range(L):
|
||||
if abs(WL[l].Xn) <= 1e-4:
|
||||
continue
|
||||
|
||||
for h in range(max_head_index):
|
||||
for s in range(S):
|
||||
if abs(v[s, h, l].Xn - 1) < 1e-4 and component_assign[l][h] != -1:
|
||||
feeder_assign[l][h] = start_slot + s * (2 if ratio == 1 else 1)
|
||||
|
||||
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(max_head_index)), quicksum(
|
||||
PU[s, l].Xn for s in range(-(max_head_index - 1) * ratio, S) for l in range(L))))
|
||||
|
||||
print('workload: ')
|
||||
for l in range(L):
|
||||
print(WL[l].Xn, end=', ')
|
||||
|
||||
print('')
|
||||
print('result')
|
||||
print('nozzle assignment: ', nozzle_assign)
|
||||
print('component assignment: ', component_assign)
|
||||
print('feeder assignment: ', feeder_assign)
|
||||
print('cycle assignment: ', cycle_assign)
|
||||
|
||||
return component_assign, feeder_assign, cycle_assign
|
||||
|
||||
|
||||
def scan_based_placement_route_generation(component_data, pcb_data, component_assign, cycle_assign):
|
||||
placement_result, head_sequence_result = [], []
|
||||
|
||||
mount_point_pos, mount_point_index, mount_point_angle, mount_point_part = [], [], [], []
|
||||
for i, data in pcb_data.iterrows():
|
||||
component_index = component_data[component_data.part == data.part].index.tolist()[0]
|
||||
# 记录贴装点序号索引和对应的位置坐标
|
||||
mount_point_index.append(i)
|
||||
mount_point_pos.append([data.x + stopper_pos[0], data.y + stopper_pos[1]])
|
||||
mount_point_angle.append(data.r)
|
||||
|
||||
mount_point_part.append(component_index)
|
||||
|
||||
lBoundary, rBoundary = min(mount_point_pos, key=lambda x: x[0])[0], max(mount_point_pos, key=lambda x: x[0])[0]
|
||||
search_step = max((rBoundary - lBoundary) / max_head_index / 2, 0)
|
||||
|
||||
ref_pos_y = min(mount_point_pos, key=lambda x: x[1])[1]
|
||||
for cycle_index, component_cycle in enumerate(component_assign):
|
||||
for _ in range(cycle_assign[cycle_index]):
|
||||
min_dist = None
|
||||
tmp_assigned_placement, tmp_assigned_head_seq = [], []
|
||||
tmp_mount_point_pos, tmp_mount_point_index = [], []
|
||||
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
|
||||
searchPoints = np.arange(lBoundary, (lBoundary + rBoundary) / 2, search_step)
|
||||
head_range = list(range(max_head_index))
|
||||
elif search_dir == 1:
|
||||
# 从右向左搜索
|
||||
searchPoints = np.arange(rBoundary + 1e-3, (lBoundary + rBoundary) / 2, -search_step)
|
||||
head_range = list(range(max_head_index - 1, -1, -1))
|
||||
else:
|
||||
# 从中间向两边搜索
|
||||
searchPoints = np.arange(lBoundary, rBoundary, search_step / 2)
|
||||
head_range, head_index = [], (max_head_index - 1) // 2
|
||||
while head_index >= 0:
|
||||
if 2 * head_index != max_head_index - 1:
|
||||
head_range.append(max_head_index - 1 - head_index)
|
||||
head_range.append(head_index)
|
||||
head_index -= 1
|
||||
|
||||
for startPoint in searchPoints:
|
||||
mount_point_pos_cpy, mount_point_index_cpy = copy.deepcopy(mount_point_pos), copy.deepcopy(
|
||||
mount_point_index)
|
||||
mount_point_angle_cpy = copy.deepcopy(mount_point_angle)
|
||||
|
||||
assigned_placement = [-1] * max_head_index
|
||||
assigned_mount_point = [[0, 0]] * max_head_index
|
||||
assigned_mount_angle = [0] * max_head_index
|
||||
head_counter, point_index = 0, -1
|
||||
for head_index in head_range:
|
||||
if head_counter == 0:
|
||||
component_index = component_assign[cycle_index][head_index]
|
||||
|
||||
if component_index == -1:
|
||||
continue
|
||||
|
||||
min_horizontal_distance = None
|
||||
for index, mount_index in enumerate(mount_point_index_cpy):
|
||||
if mount_point_part[mount_index] != component_index:
|
||||
continue
|
||||
horizontal_distance = abs(mount_point_pos_cpy[index][0] - startPoint) + 1e-3 * abs(
|
||||
mount_point_pos_cpy[index][1] - ref_pos_y)
|
||||
|
||||
if min_horizontal_distance is None or horizontal_distance < min_horizontal_distance:
|
||||
min_horizontal_distance = horizontal_distance
|
||||
point_index = index
|
||||
else:
|
||||
point_index = -1
|
||||
min_cheby_distance = None
|
||||
|
||||
next_comp_index = component_assign[cycle_index][head_index]
|
||||
if assigned_placement[head_index] != -1 or next_comp_index == -1:
|
||||
continue
|
||||
for index, mount_index in enumerate(mount_point_index_cpy):
|
||||
if mount_point_part[mount_index] != next_comp_index:
|
||||
continue
|
||||
|
||||
point_pos = [[mount_point_pos_cpy[index][0] - head_index * head_interval,
|
||||
mount_point_pos_cpy[index][1]]]
|
||||
|
||||
cheby_distance, euler_distance = 0, 0
|
||||
for next_head in range(max_head_index):
|
||||
if assigned_placement[next_head] == -1:
|
||||
continue
|
||||
point_pos.append(assigned_mount_point[next_head].copy())
|
||||
point_pos[-1][0] -= next_head * head_interval
|
||||
|
||||
point_pos = sorted(point_pos, key=lambda x: x[0])
|
||||
for mount_seq in range(len(point_pos) - 1):
|
||||
cheby_distance += max(abs(point_pos[mount_seq][0] - point_pos[mount_seq + 1][0]),
|
||||
abs(point_pos[mount_seq][1] - point_pos[mount_seq + 1][1]))
|
||||
euler_distance += math.sqrt(
|
||||
(point_pos[mount_seq][0] - point_pos[mount_seq + 1][0]) ** 2 + (
|
||||
point_pos[mount_seq][1] - point_pos[mount_seq + 1][1]) ** 2)
|
||||
|
||||
cheby_distance += 0.01 * euler_distance
|
||||
if min_cheby_distance is None or 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_placement[head_index] = mount_point_index_cpy[point_index]
|
||||
assigned_mount_point[head_index] = mount_point_pos_cpy[point_index].copy()
|
||||
assigned_mount_angle[head_index] = mount_point_angle_cpy[point_index]
|
||||
|
||||
mount_point_index_cpy.pop(point_index)
|
||||
mount_point_pos_cpy.pop(point_index)
|
||||
mount_point_angle_cpy.pop(point_index)
|
||||
|
||||
dist, head_seq = dynamic_programming_cycle_path(assigned_placement, assigned_mount_point,
|
||||
assigned_mount_angle)
|
||||
|
||||
if min_dist is None or dist < min_dist:
|
||||
tmp_mount_point_pos, tmp_mount_point_index = mount_point_pos_cpy, mount_point_index_cpy
|
||||
tmp_assigned_placement, tmp_assigned_head_seq = assigned_placement, head_seq
|
||||
min_dist = dist
|
||||
|
||||
mount_point_pos, mount_point_index = tmp_mount_point_pos, tmp_mount_point_index
|
||||
|
||||
placement_result.append(tmp_assigned_placement)
|
||||
head_sequence_result.append(tmp_assigned_head_seq)
|
||||
|
||||
return placement_result, head_sequence_result
|
||||
# return placement_route_relink_heuristic(component_data, pcb_data, placement_result, head_sequence_result)
|
||||
|
||||
|
||||
def placement_route_relink_heuristic(component_data, pcb_data, placement_result, head_sequence_result, hinter=True):
|
||||
mount_point_pos, mount_point_angle, mount_point_index, mount_point_part = [], [], [], []
|
||||
for i, data in pcb_data.iterrows():
|
||||
component_index = component_data[component_data.part == data.part].index.tolist()[0]
|
||||
# 记录贴装点序号索引和对应的位置坐标
|
||||
mount_point_index.append(i)
|
||||
mount_point_pos.append([data.x + stopper_pos[0], data.y + stopper_pos[1]])
|
||||
mount_point_angle.append(data.r)
|
||||
|
||||
mount_point_part.append(component_index)
|
||||
|
||||
cycle_length, cycle_average_pos = [], []
|
||||
for cycle, placement in enumerate(placement_result):
|
||||
prev_pos, prev_angle = None, None
|
||||
cycle_pos_list = []
|
||||
cycle_length.append(0)
|
||||
for idx, head in enumerate(head_sequence_result[cycle]):
|
||||
point_index = placement[head]
|
||||
if point_index == -1:
|
||||
continue
|
||||
pos = [mount_point_pos[point_index][0] - head * head_interval, mount_point_pos[point_index][1]]
|
||||
angle = mount_point_angle[point_index]
|
||||
cycle_pos_list.append(pos)
|
||||
if prev_pos is not None:
|
||||
if head_sequence_result[cycle][idx - 1] // 2 == head_sequence_result[cycle][idx] // 2: # 同轴
|
||||
rotary_angle = prev_angle - angle
|
||||
else:
|
||||
rotary_angle = 0
|
||||
|
||||
cycle_length[-1] += max(axis_moving_time(prev_pos[0] - pos[0], 0),
|
||||
axis_moving_time(prev_pos[1] - pos[1], 1), head_rotary_time(rotary_angle))
|
||||
prev_pos, prev_angle = pos, angle
|
||||
|
||||
cycle_average_pos.append([sum(map(lambda pos: pos[0], cycle_pos_list)) / len(cycle_pos_list),
|
||||
sum(map(lambda pos: pos[1], cycle_pos_list)) / len(cycle_pos_list)])
|
||||
|
||||
best_placement_result, best_head_sequence_result = copy.deepcopy(placement_result), copy.deepcopy(
|
||||
head_sequence_result)
|
||||
|
||||
best_cycle_length, best_cycle_average_pos = copy.deepcopy(cycle_length), copy.deepcopy(cycle_average_pos)
|
||||
|
||||
n_runningtime, n_iteration = 10, 0
|
||||
start_time = time.time()
|
||||
with tqdm(total=n_runningtime, leave=False) as pbar:
|
||||
pbar.set_description('swap heuristic process')
|
||||
prev_time = start_time
|
||||
while True:
|
||||
n_iteration += 1
|
||||
|
||||
placement_result, head_sequence_result = copy.deepcopy(best_placement_result), copy.deepcopy(
|
||||
best_head_sequence_result)
|
||||
cycle_length = best_cycle_length.copy()
|
||||
cycle_average_pos = copy.deepcopy(best_cycle_average_pos)
|
||||
|
||||
cycle_index = roulette_wheel_selection(cycle_length) # 根据周期加权移动距离随机选择周期
|
||||
|
||||
point_dist = [] # 周期内各贴装点距离中心位置的切氏距离
|
||||
for head in head_sequence_result[cycle_index]:
|
||||
point_index = placement_result[cycle_index][head]
|
||||
_delta_x = abs(mount_point_pos[point_index][0] - head * head_interval - cycle_average_pos[cycle_index][0])
|
||||
_delta_y = abs(mount_point_pos[point_index][1] - cycle_average_pos[cycle_index][1])
|
||||
point_dist.append(max(_delta_x, _delta_y))
|
||||
|
||||
# 随机选择一个异常点
|
||||
head_index = head_sequence_result[cycle_index][roulette_wheel_selection(point_dist)]
|
||||
point_index = placement_result[cycle_index][head_index]
|
||||
|
||||
# 找距离该异常点最近的周期
|
||||
min_dist = None
|
||||
chg_cycle_index = -1
|
||||
for idx in range(len(cycle_average_pos)):
|
||||
if idx == cycle_index:
|
||||
continue
|
||||
dist_ = 0
|
||||
component_type_check = False
|
||||
for head in head_sequence_result[idx]:
|
||||
dist_ += max(abs(mount_point_pos[placement_result[idx][head]][0] - mount_point_pos[point_index][0]),
|
||||
abs(mount_point_pos[placement_result[idx][head]][1] - mount_point_pos[point_index][1]))
|
||||
if mount_point_part[placement_result[idx][head]] == mount_point_part[point_index]:
|
||||
component_type_check = True
|
||||
|
||||
if (min_dist is None or dist_ < min_dist) and component_type_check:
|
||||
min_dist = dist_
|
||||
chg_cycle_index = idx
|
||||
|
||||
assert chg_cycle_index != -1
|
||||
|
||||
chg_head, min_chg_dist = None, None
|
||||
chg_cycle_point = []
|
||||
for head in head_sequence_result[chg_cycle_index]:
|
||||
index = placement_result[chg_cycle_index][head]
|
||||
chg_cycle_point.append([mount_point_pos[index][0] - head * head_interval, mount_point_pos[index][1]])
|
||||
|
||||
for idx, head in enumerate(head_sequence_result[chg_cycle_index]):
|
||||
chg_cycle_point_cpy = copy.deepcopy(chg_cycle_point)
|
||||
index = placement_result[chg_cycle_index][head]
|
||||
if mount_point_part[index] != mount_point_part[point_index]:
|
||||
continue
|
||||
chg_cycle_point_cpy[idx][0] = (mount_point_pos[index][0]) - head * head_interval
|
||||
|
||||
chg_dist = 0
|
||||
aver_chg_pos = [sum(map(lambda x: x[0], chg_cycle_point_cpy)) / len(chg_cycle_point_cpy),
|
||||
sum(map(lambda x: x[1], chg_cycle_point_cpy)) / len(chg_cycle_point_cpy)]
|
||||
|
||||
for pos in chg_cycle_point_cpy:
|
||||
chg_dist += max(abs(aver_chg_pos[0] - pos[0]), abs(aver_chg_pos[1] - pos[1]))
|
||||
|
||||
# 更换后各点距离中心更近
|
||||
if min_chg_dist is None or chg_dist < min_chg_dist:
|
||||
chg_head = head
|
||||
min_chg_dist = chg_dist
|
||||
|
||||
assert chg_head is not None
|
||||
|
||||
# === 第一轮，变更周期chg_cycle_index的贴装点重排 ===
|
||||
chg_placement_res = placement_result[chg_cycle_index].copy()
|
||||
chg_placement_res[chg_head] = point_index
|
||||
|
||||
cycle_point_list = defaultdict(list)
|
||||
for head, point in enumerate(chg_placement_res):
|
||||
if point == -1:
|
||||
continue
|
||||
cycle_point_list[mount_point_part[point]].append(point)
|
||||
|
||||
for key, point_list in cycle_point_list.items():
|
||||
cycle_point_list[key] = sorted(point_list, key=lambda p: mount_point_pos[p][0])
|
||||
|
||||
chg_placement_res, chg_point_assign_res = [], [[0, 0]] * max_head_index
|
||||
chg_angle_res = [0] * max_head_index
|
||||
for head, point_index in enumerate(placement_result[chg_cycle_index]):
|
||||
if point_index == -1:
|
||||
chg_placement_res.append(-1)
|
||||
else:
|
||||
part = mount_point_part[point_index]
|
||||
chg_placement_res.append(cycle_point_list[part][0])
|
||||
chg_point_assign_res[head] = mount_point_pos[cycle_point_list[part][0]].copy()
|
||||
chg_angle_res[head] = mount_point_angle[cycle_point_list[part][0]]
|
||||
cycle_point_list[part].pop(0)
|
||||
|
||||
chg_place_moving, chg_head_res = dynamic_programming_cycle_path(chg_placement_res, chg_point_assign_res, chg_angle_res)
|
||||
|
||||
# === 第二轮，原始周期cycle_index的贴装点重排 ===
|
||||
placement_res = placement_result[cycle_index].copy()
|
||||
placement_res[head_index] = placement_result[chg_cycle_index][chg_head]
|
||||
|
||||
for point in placement_res:
|
||||
if point == -1:
|
||||
continue
|
||||
cycle_point_list[mount_point_part[point]].append(point)
|
||||
|
||||
for key, point_list in cycle_point_list.items():
|
||||
cycle_point_list[key] = sorted(point_list, key=lambda p: mount_point_pos[p][0])
|
||||
|
||||
placement_res, point_assign_res = [], [[0, 0]] * max_head_index
|
||||
angle_assign_res = [0] * max_head_index
|
||||
for head, point_index in enumerate(placement_result[cycle_index]):
|
||||
if point_index == -1:
|
||||
placement_res.append(-1)
|
||||
else:
|
||||
part = mount_point_part[point_index]
|
||||
placement_res.append(cycle_point_list[part][0])
|
||||
point_assign_res[head] = mount_point_pos[cycle_point_list[part][0]].copy()
|
||||
angle_assign_res[head] = mount_point_angle[cycle_point_list[part][0]]
|
||||
cycle_point_list[part].pop(0)
|
||||
|
||||
place_moving, place_head_res = dynamic_programming_cycle_path(placement_res, point_assign_res, angle_assign_res)
|
||||
|
||||
# 更新贴装顺序分配结果
|
||||
placement_result[cycle_index], head_sequence_result[cycle_index] = placement_res, place_head_res
|
||||
placement_result[chg_cycle_index], head_sequence_result[chg_cycle_index] = chg_placement_res, chg_head_res
|
||||
|
||||
# 更新移动路径
|
||||
cycle_length[cycle_index], cycle_length[chg_cycle_index] = place_moving, chg_place_moving
|
||||
|
||||
# 更新平均坐标和最大偏离点索引
|
||||
point_list, point_index_list = [], []
|
||||
for head in head_sequence_result[cycle_index]:
|
||||
point_index_list.append(placement_result[cycle_index][head])
|
||||
point_pos = mount_point_pos[point_index_list[-1]].copy()
|
||||
point_pos[0] -= head * head_interval
|
||||
point_list.append(point_pos)
|
||||
|
||||
cycle_average_pos[cycle_index] = [sum(map(lambda x: x[0], point_list)) / len(point_list),
|
||||
sum(map(lambda x: x[1], point_list)) / len(point_list)]
|
||||
|
||||
point_list, point_index_list = [], []
|
||||
for head in head_sequence_result[chg_cycle_index]:
|
||||
point_index_list.append(placement_result[chg_cycle_index][head])
|
||||
point_pos = mount_point_pos[point_index_list[-1]].copy()
|
||||
point_pos[0] -= head * head_interval
|
||||
point_list.append(point_pos)
|
||||
|
||||
cycle_average_pos[chg_cycle_index] = [sum(map(lambda x: x[0], point_list)) / len(point_list),
|
||||
sum(map(lambda x: x[1], point_list)) / len(point_list)]
|
||||
|
||||
if sum(cycle_length) < sum(best_cycle_length):
|
||||
best_cycle_length = cycle_length.copy()
|
||||
best_cycle_average_pos = copy.deepcopy(cycle_average_pos)
|
||||
best_placement_result, best_head_sequence_result = copy.deepcopy(placement_result), copy.deepcopy(
|
||||
head_sequence_result)
|
||||
|
||||
cur_time = time.time()
|
||||
if cur_time - start_time > n_runningtime:
|
||||
break
|
||||
|
||||
pbar.update(cur_time - prev_time)
|
||||
prev_time = cur_time
|
||||
|
||||
# print("number of iteration: ", n_iteration)
|
||||
return best_placement_result, best_head_sequence_result
|
||||
Reference in New Issue
Block a user