import time import pandas as pd from dataloader import * from lineopt_genetic import line_optimizer_genetic from lineopt_heuristic import line_optimizer_heuristic from lineopt_reconfiguration import line_optimizer_reconfiguration from lineopt_hyperheuristic import line_optimizer_hyperheuristic from lineopt_model import line_optimizer_model from base_optimizer.optimizer_interface import * def optimizer(pcb_data, component_data, feeder_data, params): if params.machine_number == 1: assembly_info = [base_optimizer(1, pcb_data, component_data, feeder_data, params, hinter=True)] return assembly_info if params.line_optimizer == 'hyper-heuristic' or params.line_optimizer == 'heuristic' or params.line_optimizer \ == 'genetic' or params.line_optimizer == 'reconfiguration': if params.line_optimizer == 'hyper-heuristic': assignment_result = line_optimizer_hyperheuristic(component_data, pcb_data, params.machine_number) elif params.line_optimizer == "heuristic": assignment_result = line_optimizer_heuristic(component_data, params.machine_number) elif params.line_optimizer == "genetic": assignment_result = line_optimizer_genetic(component_data, params.machine_number) else: assignment_result = line_optimizer_reconfiguration(component_data, pcb_data, params.machine_number) partial_pcb_data, partial_component_data = convert_line_assigment(pcb_data, component_data, assignment_result) assembly_info = [] for machine_index in range(params.machine_number): assembly_info.append(base_optimizer(machine_index + 1, partial_pcb_data[machine_index], partial_component_data[machine_index], pd.DataFrame(columns=['slot', 'part']), params, hinter=True)) elif params.line_optimizer == 'mip-model': assembly_info = line_optimizer_model(component_data, pcb_data, params.machine_number) else: raise 'line optimizer method is not existed' return assembly_info @timer_wrapper def main(): # 参数解析 parser = argparse.ArgumentParser(description='assembly line optimizer implementation') parser.add_argument('--mode', default=1, type=int, help='mode: 0 -directly load pcb data without optimization ' 'for data analysis, 1 -optimize pcb data, 2 -batch test') parser.add_argument('--filename', default='PCB.txt', type=str, help='load pcb data') # parser.add_argument('--filename', default='chapter3-2/PCB2-8 Arg1.txt', type=str, help='load pcb data') parser.add_argument('--comp_register', default=1, type=int, help='register the component according the pcb data') parser.add_argument('--machine_number', default=1, type=int, help='the number of machine in the assembly line') # parser.add_argument('--machine_optimizer', default='mip-model', type=str, help='optimizer for single machine') parser.add_argument('--machine_optimizer', default='feeder-priority', type=str, help='optimizer for single machine') parser.add_argument('--line_optimizer', default='hyper-heuristic', type=str, help='optimizer for PCB assembly line') # parser.add_argument('--line_optimizer', default='model', 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') parser.add_argument('--save', default=0, type=int, help='save the optimization result') parser.add_argument('--save_suffix', default='(10)', type=str, help='load pcb data') 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) if params.mode == 0: partial_pcb_data, partial_component_data, _ = load_data(params.filename, load_feeder=False) assembly_info = [] for machine_index in range(len(partial_pcb_data)): opt_res = convert_pcbdata_to_result(partial_pcb_data[machine_index], partial_component_data[machine_index]) info = placement_info_evaluation(partial_component_data[machine_index], partial_pcb_data[machine_index], opt_res) assembly_info.append(info) optimization_assign_result(partial_component_data[machine_index], partial_pcb_data[machine_index], opt_res, nozzle_hinter=True, component_hinter=True, feeder_hinter=True) info.print() if params.save: output_optimize_result(f'result/{params.filename[:-4]}-T-Solution-M0{machine_index + 1}', partial_component_data[machine_index], partial_pcb_data[machine_index], opt_res) print('------------------------------ ') for machine_idx, info in enumerate(assembly_info): print(f'assembly time for machine {machine_idx + 1: d}: {info.total_time: .3f} s, total placement: ' f'{info.total_points}, total component types {info.total_components: d}') print(f'finial assembly time: {max(info.total_time for info in assembly_info): .3f} s, ' f'standard deviation: {np.std([info.total_time for info in assembly_info]): .3f}') elif params.mode == 1: # sys.stdout = open(f'record/{params.filename[:-4]}-{params.line_optimizer}.txt', 'w') # 加载PCB数据 partial_pcb_data, partial_component_data, feeder_data = load_data(params.filename, load_feeder=True) pcb_data, component_data = merge_data(partial_pcb_data, partial_component_data) start_time = time.time() assembly_info = optimizer(pcb_data, component_data, feeder_data, params) # sys.stdout = sys.__stdout__ print(f'optimizer running time: {time.time() - start_time: .3f}') for machine_idx, info in enumerate(assembly_info): print(f'assembly time for machine {machine_idx + 1: d}: {info.total_time: .3f} s, total placement: ' f'{info.total_points}, total component types {info.total_components: d}') print(f'assembly metric evaluation： {max(info.metric() for info in assembly_info)}') print(f'finial assembly time: {max(info.total_time for info in assembly_info): .3f} s, ' f'standard deviation: {np.std([info.total_time for info in assembly_info]): .3f}') elif params.mode == 2: machine_optimizer = ['two-phase', 'hybrid-genetic', 'cell-division', 'feeder-priority', 'aggregation'] running_round = 10 opt_columns = ['Cycle', 'Pick', 'Nozzle-Change', 'Running-Time'] opt_result, opt_runtime = defaultdict(pd.DataFrame), defaultdict(pd.DataFrame) for opt in machine_optimizer: opt_result[opt] =pd.DataFrame(columns=opt_columns) opt_result[opt].index.name = 'file' for _, file in enumerate(os.listdir('data/')): if file[-3:] != 'txt': continue partial_pcb_data, partial_component_data, feeder_data = load_data(file) pcb_data, component_data = merge_data(partial_pcb_data, partial_component_data) for opt in machine_optimizer: for round_idx in range(running_round): print(f'--- file ： {file}, round ： {round_idx}, optimizer ： {opt} --- ') params = parser.parse_args(['--machine_optimizer', opt, '--machine_number', str(1)]) start_time = time.time() assembly_info = optimizer(pcb_data, component_data, feeder_data, params) opt_result[opt].loc[file + str(round_idx + 1), 'Cycle'] = assembly_info[0].cycle_counter opt_result[opt].loc[file + str(round_idx + 1), 'Pick'] = assembly_info[0].pickup_counter opt_result[opt].loc[file + str(round_idx + 1), 'Nozzle-Change'] = assembly_info[0].nozzle_change_counter opt_result[opt].loc[file + str(round_idx + 1), 'Running-Time'] = time.time() - start_time with pd.ExcelWriter('result/machine_optimizer.xlsx', engine='openpyxl') as writer: for opt, result in opt_result.items(): result.to_excel(writer, sheet_name=opt, float_format='%.3f', na_rep='') else: # line_optimizer = ['T-Solution', 'hyper-heuristic', 'genetic', 'reconfiguration'] line_optimizer = ['genetic'] file_dirs = ['L01', 'L02', 'L03'] running_round = 10 line_opt_result, line_opt_runtime = defaultdict(pd.DataFrame), defaultdict(pd.DataFrame) opt_columns = [] for line_opt in line_optimizer: if line_opt == 'T-Solution': opt_columns.append(line_opt) else: opt_columns.extend([line_opt + str(idx + 1) for idx in range(running_round)]) for file_dir in file_dirs: line_opt_result[file_dir] = pd.DataFrame(columns=opt_columns) line_opt_runtime[file_dir] = pd.DataFrame(columns=opt_columns) line_opt_result[file_dir].index.name, line_opt_runtime[file_dir].index.name = 'file', 'file' for file_index, file in enumerate(os.listdir('data/' + file_dir)): sys.stdout = sys.__stdout__ print(f'--- {file_dir} : ({file_index + 1}) file ： {file} --- ') try: partial_pcb_data, partial_component_data, _ = load_data(file_dir + '/' + file, load_feeder=False) except: traceback.print_exc() warning_info = f'file: {file_dir}/{file}: an unexpected error occurs for data loader' warnings.warn(warning_info, SyntaxWarning) continue machine_number = len(partial_pcb_data) if not os.path.exists(f'record/{file_dir}'): os.makedirs(f'record/{file_dir}') merge_pcb_data, merge_component_data = merge_data(partial_pcb_data, partial_component_data) for line_opt in line_optimizer: assembly_info = [] if line_opt == 'T-Solution': sys.stdout = open(f'record/{file_dir}/{file[:-4]}-{line_opt}.txt', 'w') for machine_index in range(machine_number): opt_res = convert_pcbdata_to_result(partial_pcb_data[machine_index], partial_component_data[machine_index]) print('----- Placement machine ' + str(machine_index + 1) + ' ----- ') info = placement_info_evaluation(partial_component_data[machine_index], partial_pcb_data[machine_index], opt_res, hinter=True) print('------------------------------ ') assembly_info.append(info) if params.save: output_optimize_result(f'result/{file_dir}/{file[:-4]}-T-Solution-M0{machine_index + 1}', partial_component_data[machine_index], partial_pcb_data[machine_index], opt_res) line_opt_result[file_dir].loc[file, line_opt] = max(info.total_time for info in assembly_info) for machine_idx, info in enumerate(assembly_info): print( f'assembly time for machine {machine_idx + 1: d}: {info.total_time: .3f} s, total placement: ' f'{info.total_points}, total component types {info.total_components: d}') print(f'finial assembly time: {max(info.total_time for info in assembly_info): .3f} s, ' f'standard deviation: {np.std([info.total_time for info in assembly_info]): .3f}') else: for round_idx in range(running_round): sys.stdout = open(f'record/{file_dir}/{file[:-4]}-{line_opt} ({round_idx + 1}).txt', 'w') params = parser.parse_args( ['--filename', file_dir + '/' + file, '--machine_number', str(machine_number), '--line_optimizer', line_opt, '--save_suffix', f'({round_idx + 1})']) start_time = time.time() assembly_info = optimizer(merge_pcb_data, merge_component_data, params) line_opt_result[file_dir].loc[file, line_opt + str(round_idx + 1)] = max( info.total_time for info in assembly_info) line_opt_runtime[file_dir].loc[file, line_opt + str(round_idx + 1)] = time.time() - start_time for machine_idx, info in enumerate(assembly_info): print( f'assembly time for machine {machine_idx + 1: d}: {info.total_time: .3f} s, ' f'total placement: {info.total_points}, ' f'total component types {info.total_components: d}') print(f'finial assembly time: {max(info.total_time for info in assembly_info): .3f} s, ' f'standard deviation: {np.std([info.total_time for info in assembly_info]): .3f}') with pd.ExcelWriter('result/line_optimizer.xlsx', engine='openpyxl') as writer: for file_dir, result in line_opt_result.items(): result.to_excel(writer, sheet_name='result-' + file_dir, float_format='%.3f', na_rep='') for file_dir, running_time in line_opt_runtime.items(): running_time.to_excel(writer, sheet_name='running_time-' + file_dir, float_format='%.3f', na_rep='') if __name__ == '__main__': main()