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