256 lines
12 KiB
Python
256 lines
12 KiB
Python
import copy
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import math
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import matplotlib.pyplot as plt
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import pandas as pd
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from base_optimizer.optimizer_aggregation import *
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from base_optimizer.optimizer_scanbased import *
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from base_optimizer.optimizer_celldivision import *
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from base_optimizer.optimizer_hybridgenetic import *
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from base_optimizer.optimizer_feederpriority import *
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from dataloader import *
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from optimizer_genetic import *
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from optimizer_heuristic import *
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def deviation(data):
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assert len(data) > 0
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average, variance = sum(data) / len(data), 0
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for v in data:
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variance += (v - average) ** 2
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return variance / len(data)
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def optimizer(pcb_data, component_data, assembly_line_optimizer, single_machine_optimizer):
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# 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
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assignment_result = assemblyline_optimizer_heuristic(pcb_data, component_data)
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# assignment_result = assemblyline_optimizer_genetic(pcb_data, component_data)
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print(assignment_result)
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assignment_result_cpy = copy.deepcopy(assignment_result)
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placement_points, placement_time = [], []
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partial_pcb_data, partial_component_data = defaultdict(pd.DataFrame), defaultdict(pd.DataFrame)
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for machine_index in range(max_machine_index):
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partial_pcb_data[machine_index] = pd.DataFrame(columns=pcb_data.columns)
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partial_component_data[machine_index] = component_data.copy(deep=True)
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placement_points.append(sum(assignment_result[machine_index]))
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assert sum(placement_points) == len(pcb_data)
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# === averagely assign available feeder ===
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for part_index, data in component_data.iterrows():
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feeder_limit = data['feeder-limit']
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feeder_points = [assignment_result[machine_index][part_index] for machine_index in range(max_machine_index)]
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for machine_index in range(max_machine_index):
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if feeder_points[machine_index] == 0:
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continue
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arg_feeder = max(math.floor(feeder_points[machine_index] / sum(feeder_points) * data['feeder-limit']), 1)
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partial_component_data[machine_index].loc[part_index]['feeder-limit'] = arg_feeder
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feeder_limit -= arg_feeder
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for machine_index in range(max_machine_index):
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if feeder_limit <= 0:
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break
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if feeder_points[machine_index] == 0:
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continue
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partial_component_data[machine_index].loc[part_index]['feeder-limit'] += 1
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feeder_limit -= 1
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for machine_index in range(max_machine_index):
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if feeder_points[machine_index] > 0:
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assert partial_component_data[machine_index].loc[part_index]['feeder-limit'] > 0
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# === assign placements ===
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component_machine_index = [0 for _ in range(len(component_data))]
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for _, data in pcb_data.iterrows():
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part_index = component_data[component_data['part'] == data['part']].index.tolist()[0]
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while True:
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machine_index = component_machine_index[part_index]
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if assignment_result[machine_index][part_index] == 0:
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component_machine_index[part_index] += 1
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machine_index += 1
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else:
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break
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assignment_result[machine_index][part_index] -= 1
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partial_pcb_data[machine_index] = pd.concat([partial_pcb_data[machine_index], pd.DataFrame(data).T])
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# === adjust the number of available feeders for single optimization separately ===
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for machine_index, data in partial_pcb_data.items():
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data = data.reset_index(drop=True)
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if len(data) == 0:
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continue
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part_info = [] # part info list：(part index, part points, available feeder-num, upper feeder-num)
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for part_index, cp_data in partial_component_data[machine_index].iterrows():
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if assignment_result_cpy[machine_index][part_index]:
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part_info.append(
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[part_index, assignment_result_cpy[machine_index][part_index], 1, cp_data['feeder-limit']])
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part_info = sorted(part_info, key=lambda x: x[1], reverse=True)
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start_index, end_index = 0, min(max_head_index - 1, len(part_info) - 1)
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while start_index < len(part_info):
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assign_part_point, assign_part_index = [], []
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for idx_ in range(start_index, end_index + 1):
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for _ in range(part_info[idx_][2]):
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assign_part_point.append(part_info[idx_][1] / part_info[idx_][2])
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assign_part_index.append(idx_)
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variance = deviation(assign_part_point)
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while start_index != end_index:
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part_info_index = assign_part_index[np.argmax(assign_part_point)]
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if part_info[part_info_index][2] < part_info[part_info_index][3]: # 供料器数目上限的限制
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part_info[part_info_index][2] += 1
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end_index -= 1
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new_assign_part_point, new_assign_part_index = [], []
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for idx_ in range(start_index, end_index + 1):
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for _ in range(part_info[idx_][2]):
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new_assign_part_point.append(part_info[idx_][1] / part_info[idx_][2])
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new_assign_part_index.append(idx_)
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new_variance = deviation(new_assign_part_point)
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if variance < new_variance:
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part_info[part_info_index][2] -= 1
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end_index += 1
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break
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variance = new_variance
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assign_part_index, assign_part_point = new_assign_part_index, new_assign_part_point
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else:
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break
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start_index = end_index + 1
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end_index = min(start_index + max_head_index - 1, len(part_info) - 1)
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# update available feeder number
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max_avl_feeder = max(part_info, key=lambda x: x[2])[2]
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for info in part_info:
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partial_component_data[machine_index].loc[info[0]]['feeder-limit'] = math.ceil(info[2] / max_avl_feeder)
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placement_time.append(base_optimizer(machine_index + 1, data, partial_component_data[machine_index],
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feeder_data=pd.DataFrame(columns=['slot', 'part', 'arg']),
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method=single_machine_optimizer, hinter=True))
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average_time, standard_deviation_time = sum(placement_time) / max_machine_index, 0
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for machine_index in range(max_machine_index):
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print('assembly time for machine ' + str(machine_index + 1) + ': ' + str(
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placement_time[machine_index]) + ' s, ' + 'total placements: ' + str(placement_points[machine_index]))
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standard_deviation_time += pow(placement_time[machine_index] - average_time, 2)
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standard_deviation_time /= max_machine_index
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standard_deviation_time = math.sqrt(standard_deviation_time)
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print('finial assembly time: ' + str(max(placement_time)) + 's, standard deviation: ' + str(standard_deviation_time))
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# todo: 不同类型元件的组装时间差异
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def base_optimizer(machine_index, pcb_data, component_data, feeder_data=None, method='', hinter=False):
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if method == 'cell_division': # 基于元胞分裂的遗传算法
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component_result, cycle_result, feeder_slot_result = optimizer_celldivision(pcb_data, component_data,
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hinter=False)
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placement_result, head_sequence = greedy_placement_route_generation(component_data, pcb_data, component_result,
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cycle_result, feeder_slot_result)
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elif method == 'feeder_scan': # 基于基座扫描的供料器优先算法
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# 第1步：分配供料器位置
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nozzle_pattern = feeder_allocate(component_data, pcb_data, feeder_data, figure=False)
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# 第2步：扫描供料器基座，确定元件拾取的先后顺序
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component_result, cycle_result, feeder_slot_result = feeder_base_scan(component_data, pcb_data, feeder_data,
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nozzle_pattern)
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# 第3步：贴装路径规划
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placement_result, head_sequence = greedy_placement_route_generation(component_data, pcb_data, component_result,
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cycle_result, feeder_slot_result)
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# placement_result, head_sequence = beam_search_for_route_generation(component_data, pcb_data, component_result,
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# cycle_result, feeder_slot_result)
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elif method == 'hybrid_genetic': # 基于拾取组的混合遗传算法
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component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = optimizer_hybrid_genetic(
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pcb_data, component_data, hinter=False)
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elif method == 'aggregation': # 基于batch-level的整数规划 + 启发式算法
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component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = optimizer_aggregation(
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component_data, pcb_data)
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elif method == 'genetic_scanning':
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component_result, cycle_result, feeder_slot_result, placement_result, head_sequence = optimizer_genetic_scanning(
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component_data, pcb_data, hinter=False)
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else:
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raise 'method is not existed'
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if hinter:
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optimization_assign_result(component_data, pcb_data, component_result, cycle_result, feeder_slot_result,
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nozzle_hinter=True, component_hinter=False, feeder_hinter=False)
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print('----- Placement machine ' + str(machine_index) + ' ----- ')
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print('-Cycle counter: {}'.format(sum(cycle_result)))
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total_nozzle_change_counter, total_pick_counter = 0, 0
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total_pick_movement = 0
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assigned_nozzle = ['' if idx == -1 else component_data.loc[idx]['nz'] for idx in component_result[0]]
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for cycle in range(len(cycle_result)):
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pick_slot = set()
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for head in range(max_head_index):
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if (idx := component_result[cycle][head]) == -1:
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continue
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nozzle = component_data.loc[idx]['nz']
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if nozzle != assigned_nozzle[head]:
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if assigned_nozzle[head] != '':
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total_nozzle_change_counter += 1
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assigned_nozzle[head] = nozzle
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pick_slot.add(feeder_slot_result[cycle][head] - head * interval_ratio)
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total_pick_counter += len(pick_slot) * cycle_result[cycle]
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pick_slot = list(pick_slot)
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pick_slot.sort()
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for idx in range(len(pick_slot) - 1):
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total_pick_movement += abs(pick_slot[idx+1] - pick_slot[idx])
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print('-Nozzle change counter: {}'.format(total_nozzle_change_counter))
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print('-Pick operation counter: {}'.format(total_pick_counter))
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print('-Pick movement: {}'.format(total_pick_movement))
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print('------------------------------ ')
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# 估算贴装用时
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return placement_time_estimate(component_data, pcb_data, component_result, cycle_result, feeder_slot_result,
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placement_result, head_sequence, hinter=False)
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@timer_wrapper
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def main():
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# warnings.simplefilter('ignore')
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# 参数解析
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parser = argparse.ArgumentParser(description='assembly line optimizer implementation')
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parser.add_argument('--filename', default='PCB.txt', type=str, help='load pcb data')
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parser.add_argument('--auto_register', default=1, type=int, help='register the component according the pcb data')
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parser.add_argument('--base_optimizer', default='feeder_scan', type=str, help='base optimizer for single machine')
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parser.add_argument('--assembly_optimizer', default='heuristic', type=str, help='optimizer for PCB Assembly Line')
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parser.add_argument('--feeder_limit', default=3, type=int,
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help='the upper feeder limit for each type of component')
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params = parser.parse_args()
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# 结果输出显示所有行和列
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pd.set_option('display.max_columns', None)
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pd.set_option('display.max_rows', None)
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# 加载PCB数据
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pcb_data, component_data, _ = load_data(params.filename, default_feeder_limit=params.feeder_limit,
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cp_auto_register=params.auto_register) # 加载PCB数据
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optimizer(pcb_data, component_data, params.assembly_optimizer, params.base_optimizer)
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if __name__ == '__main__':
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main()
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