117 lines
5.8 KiB
Python
117 lines
5.8 KiB
Python
import random
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import numpy as np
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from dataloader import *
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from optimizer_genetic import line_optimizer_genetic
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from optimizer_heuristic import line_optimizer_heuristic
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from optimizer_reconfiguration import line_optimizer_reconfiguration
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from optimizer_hyperheuristic import line_optimizer_hyperheuristic
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from base_optimizer.optimizer_interface import *
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def optimizer(pcb_data, component_data, line_optimizer, machine_optimizer, machine_number):
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if machine_number > 1:
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if line_optimizer == 'hyper-heuristic':
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assignment_result = line_optimizer_hyperheuristic(component_data, pcb_data, machine_number)
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elif line_optimizer == "heuristic":
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assignment_result = line_optimizer_heuristic(component_data, machine_number)
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elif line_optimizer == "genetic":
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assignment_result = line_optimizer_genetic(component_data, machine_number)
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elif line_optimizer == "reconfiguration":
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assignment_result = line_optimizer_reconfiguration(component_data, pcb_data, machine_number)
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else:
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raise 'line optimizer method is not existed'
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else:
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assignment_result = [[]]
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for _, data in component_data.iterrows():
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assignment_result[-1].append(data.points)
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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(machine_number):
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assembly_info.append(
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base_optimizer(machine_index + 1, partial_pcb_data[machine_index], partial_component_data[machine_index],
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feeder_data=pd.DataFrame(columns=['slot', 'part', 'arg']), method=machine_optimizer,
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hinter=True))
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for machine_index in range(machine_number):
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total_component_types = sum(1 if pt else 0 for pt in assignment_result[machine_index])
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total_placement_points = sum(assignment_result[machine_index])
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total_time = assembly_info[machine_index].total_time
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print(f'assembly time for machine {machine_index + 1: d}: {total_time: .3f} s, total placement: '
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f'{total_placement_points}, total component types {total_component_types: d}', end='')
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for part_index in range(len(assignment_result[machine_index])):
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if assignment_result[machine_index][part_index]:
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print(', ', part_index, end='')
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print('')
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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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@timer_wrapper
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def main():
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warnings.simplefilter(action='ignore', category=FutureWarning)
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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('--machine_number', default=3, type=int, help='the number of machine in the assembly line')
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parser.add_argument('--machine_optimizer', default='feeder-scan', 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='genetic', 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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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, load_feeder_data=False) # 加载PCB数据
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optimizer(pcb_data, component_data, params.line_optimizer, params.machine_optimizer, params.machine_number)
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# index_list, part_list = [1, 4, 8, 9, 12, 13, 14, 18, 20, 22, 23, 25, 33, 35, 38, 39, 40], []
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# for idx in index_list:
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# part_list.append(component_data.iloc[idx].part)
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# pcb_data = pcb_data[pcb_data['part'].isin(part_list)].reset_index(drop=True)
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# component_data = component_data.iloc[index_list].reset_index(drop=True)
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# optimizer(pcb_data, component_data, params.line_optimizer, params.machine_optimizer, 1)
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#
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# from optimizer_hyperheuristic import DataMgr, Net
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# data_mgr = DataMgr()
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# cp_points, cp_nozzle = defaultdict(int), defaultdict(str)
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# for _, data in component_data.iterrows():
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# cp_points[data.part], cp_nozzle[data.part] = data.points, data.nz
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# idx = 1832
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# data = data_mgr.loader(file_name)
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# encoding = np.array(data[0][idx])
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# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# net = Net(input_size=data_mgr.get_feature(), output_size=1).to(device)
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#
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# net.load_state_dict(torch.load('model/net_model.pth'))
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# board_width, board_height = pcb_data['x'].max() - pcb_data['x'].min(), pcb_data['y'].max() - pcb_data['y'].min()
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# encoding = np.array(data_mgr.encode(cp_points, cp_nozzle, board_width, board_height))
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# encoding = torch.from_numpy(encoding.reshape((-1, np.shape(encoding)[0]))).float().to("cuda")
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# print(f'net pred time: {net(encoding)[0, 0].item():.3f}')
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# with open('model/lr_model.pkl', 'rb') as f:
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# lr = pickle.load(f)
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#
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# print('lr model train data: ', np.array(data[2:]).T[idx].reshape(1, -1))
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# print('lr model pred time: ', lr.predict(np.array(data[2:]).T[idx].reshape(1, -1)))
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# print('real time: ', data[-1][idx] * 3600 / data[1][idx])
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if __name__ == '__main__':
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main()
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