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) # assignment_result = [[0, 0, 0, 0, 216, 0, 0], [0, 0, 0, 0, 216, 0, 0], [36, 24, 12, 12, 0, 36, 12]] placement_points, placement_time = [], [] partial_pcb_data, partial_component_data = defaultdict(pd.DataFrame), defaultdict(pd.DataFrame) for machine_index in range(max_machine_index): partial_pcb_data[machine_index] = pd.DataFrame(columns=pcb_data.columns) partial_component_data[machine_index] = component_data.copy(deep=True) placement_points.append(sum(assignment_result[machine_index])) # 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)] for machine_index in range(max_machine_index): if feeder_points[machine_index] == 0: continue arg_feeder = max(math.floor(feeder_points[machine_index] / sum(feeder_points) * data['feeder-limit']), 1) partial_component_data[machine_index].loc[part_index]['feeder-limit'] = arg_feeder feeder_limit -= arg_feeder for machine_index in range(max_machine_index): if feeder_limit <= 0: break if feeder_points[machine_index] == 0: continue partial_component_data[machine_index].loc[part_index]['feeder-limit'] += 1 feeder_limit -= 1 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] while True: machine_index = component_machine_index[part_index] if assignment_result[machine_index][part_index] == 0: component_machine_index[part_index] += 1 machine_index += 1 else: break assignment_result[machine_index][part_index] -= 1 partial_pcb_data[machine_index] = pd.concat([partial_pcb_data[machine_index], pd.DataFrame(data).T]) for machine_index, data in partial_pcb_data.items(): data = data.reset_index(drop=True) if len(data) == 0: continue 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)) average_time, standard_deviation_time = sum(placement_time) / max_machine_index, 0 for machine_index in range(max_machine_index): print('assembly time for machine ' + str(machine_index + 1) + ': ' + str( placement_time[machine_index]) + ' s, ' + 'total placements: ' + str(placement_points[machine_index])) standard_deviation_time += pow(placement_time[machine_index] - average_time, 2) standard_deviation_time /= max_machine_index standard_deviation_time = math.sqrt(standard_deviation_time) print('finial assembly time: ' + str(max(placement_time)) + 's, standard deviation: ' + str(standard_deviation_time)) # 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) 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 # 第1步：分配供料器位置 nozzle_pattern = feeder_allocate(component_data, pcb_data, feeder_data, 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) # 第3步：贴装路径规划 placement_result, head_sequence = greedy_placement_route_generation(component_data, pcb_data, component_result, cycle_result, feeder_slot_result) # placement_result, head_sequence = beam_search_for_route_generation(component_data, pcb_data, component_result, # cycle_result, feeder_slot_result) 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) 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) 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) print('----- Placement machine ' + str(machine_index) + ' ----- ') print('-Cycle counter: {}'.format(sum(cycle_result))) total_nozzle_change_counter, total_pick_counter = 0, 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)): pick_slot = set() for head in range(max_head_index): if (idx := component_result[cycle][head]) == -1: continue nozzle = component_data.loc[idx]['nz'] if nozzle != assigned_nozzle[head]: if assigned_nozzle[head] != '': total_nozzle_change_counter += 1 assigned_nozzle[head] = nozzle pick_slot.add(feeder_slot_result[cycle][head] - head * interval_ratio) total_pick_counter += len(pick_slot) * cycle_result[cycle] print('-Nozzle change counter: {}'.format(total_nozzle_change_counter)) print('-Pick operation counter: {}'.format(total_pick_counter)) 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) 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('--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, help='the upper feeder limit for each type of component') params = parser.parse_args() # 结果输出显示所有行和列 pd.set_option('display.max_columns', None) pd.set_option('display.max_rows', None) # 加载PCB数据 pcb_data, component_data, _ = load_data(params.filename, default_feeder_limit=params.feeder_limit, cp_auto_register=params.auto_register) # 加载PCB数据 optimizer(pcb_data, component_data, params.assembly_optimizer, params.base_optimizer) if __name__ == '__main__': main()