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在代价函数中增加拾取次数计算

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hit_lu
2023-08-03 00:26:40 +08:00
parent 6e56f796f0
commit d5e8bbcc87
1 changed files with 50 additions and 9 deletions
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optimizer_genetic.py
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@ -1,14 +1,16 @@
# implementation of <<An integrated allocation method for the PCB assembly line balancing problem with nozzle changes>>
import copy
import matplotlib.pyplot as plt
from base_optimizer.optimizer_common import *
def selective_initialization(component_points, component_feeders, population_size):
population = [] # population initialization
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 +52,7 @@ 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]
@ -134,9 +136,10 @@ def selective_crossover(component_points, component_feeders, mother, father, non
def cal_individual_val(component_points, component_nozzle, individual):
idx, objective_val = 0, []
machine_component_points = [[] for _ in range(max_machine_index)]
nozzle_component_points = defaultdict(list)
# decode the component allocation
for points in component_points.values():
for comp_idx, 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:
@ -149,6 +152,11 @@ def cal_individual_val(component_points, component_nozzle, individual):
machine_component_points[-1].append(component_counter)
idx += (points + max_machine_index - 1)
nozzle_component_points[component_nozzle[comp_idx]] = [0] * len(component_points) # 初始化元件-吸嘴点数列表
for comp_idx, points in component_points:
nozzle_component_points[component_nozzle[comp_idx]][comp_idx] = points
for machine_idx in range(max_machine_index):
nozzle_points = defaultdict(int)
for idx, nozzle in component_nozzle.items():
@ -166,6 +174,8 @@ def cal_individual_val(component_points, component_nozzle, individual):
total_heads = (1 + ul) * max_head_index - len(nozzle_points)
nozzle_heads = defaultdict(int)
for nozzle in nozzle_points.keys():
if nozzle_points[nozzle] == 0:
continue
nozzle_heads[nozzle] = math.floor(nozzle_points[nozzle] * 1.0 / machine_points * total_heads)
nozzle_heads[nozzle] += 1
@ -173,7 +183,8 @@ def cal_individual_val(component_points, component_nozzle, individual):
for heads in nozzle_heads.values():
total_heads -= heads
for nozzle in sorted(nozzle_heads, key=lambda x: nozzle_points[x] / nozzle_heads[x], reverse=True):
while True:
nozzle = max(nozzle_heads, key=lambda x: nozzle_points[x] / nozzle_heads[x])
if total_heads == 0:
break
nozzle_heads[nozzle] += 1
@ -193,10 +204,38 @@ def cal_individual_val(component_points, component_nozzle, individual):
heads_placement[idx][1] += 1
heads_placement = sorted(heads_placement, key=lambda x: x[1], reverse=True)
# the number of pick-up operations
# (under the assumption of the number of feeder available for each comp. type is equal 1)
pl = 0
heads_placement_points = [0 for _ in range(max_head_index)]
while True:
head_assign_point = []
for head in range(max_head_index):
if heads_placement_points[head] != 0 or heads_placement[head] == 0:
continue
nozzle, points = heads_placement[head]
max_comp_index = np.argmax(nozzle_component_points[nozzle])
heads_placement_points[head] = min(points, nozzle_component_points[nozzle][max_comp_index])
nozzle_component_points[nozzle][max_comp_index] -= heads_placement_points[head]
head_assign_point.append(heads_placement_points[head])
min_points_list = list(filter(lambda x: x > 0, heads_placement_points))
if len(min_points_list) == 0 or len(head_assign_point) == 0:
break
pl += max(head_assign_point)
for head in range(max_head_index):
heads_placement[head][1] -= min(min_points_list)
heads_placement_points[head] -= min(min_points_list)
# every max_head_index heads in the non-decreasing order are grouped together as nozzle set
for idx in range(len(heads_placement) // max_head_index):
wl += heads_placement[idx][1]
objective_val.append(T_pp * machine_points + T_tr * wl + T_nc * ul)
objective_val.append(T_pp * machine_points + T_tr * wl + T_nc * ul + T_pl * pl)
return objective_val, machine_component_points
@ -219,13 +258,15 @@ 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)
with tqdm(total=n_generations) as pbar:
pbar.set_description('genetic algorithm process for PCB assembly line balance')
new_population, new_pop_val = [], []
new_population = []
for _ in range(n_generations):
# calculate fitness value
pop_val = []
@ -267,7 +308,7 @@ def assemblyline_optimizer_genetic(pcb_data, component_data):
offspring1 = constraint_swap_mutation(component_points, offspring1)
if np.random.random() < mutation_rate:
offspring1 = constraint_swap_mutation(component_points, offspring1)
offspring2 = constraint_swap_mutation(component_points, offspring2)
new_population.append(offspring1)
new_population.append(offspring2)