LU/assembly-line-optimizer
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增加了HSMO整线优化方法，读取数据增加了供料器部分

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hit-lu
2025-08-10 16:58:42 +08:00
parent 045f2f394d
commit 4fd5560650
17 changed files with 1765 additions and 352 deletions
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20
estimator.py
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@@ -6,9 +6,9 @@ def exact_assembly_time(pcb_data, component_data):
feeder_data = pd.DataFrame(columns=['slot', 'part'])
component_result, cycle_result, feeder_slot_result = feeder_priority_assignment(component_data, pcb_data,
feeder_data, hinter=False)
placement_result, head_sequence_result = greedy_placement_route_generation(component_data, pcb_data,
component_result, cycle_result,
feeder_slot_result, hinter=False)
placement_result, head_sequence_result = place_allocate_sequence_route_generation(component_data, pcb_data,
component_result, cycle_result,
feeder_slot_result, hinter=False)
opt_res = OptResult(component_result, cycle_result, feeder_slot_result, placement_result, head_sequence_result)
info = placement_info_evaluation(component_data, pcb_data, opt_res)
# return info.metric()
@@ -19,7 +19,7 @@ def error_info(pred_val, real_val, type='train'):
absolute_error = np.array([])
for idx, (t1, t2) in enumerate(np.nditer([pred_val, real_val])):
absolute_error = np.append(absolute_error, abs(t1 - t2) / (t2 + 1e-10) * 100)
if absolute_error[-1] > 15:
if absolute_error[-1] > 10:
print(f'\033[0;31;31midx: {idx + 1: d}, net: {t1: .3f}, real: {t2: .3f}, '
f'gap: {absolute_error[-1]: .3f}\033[0m')
@@ -561,6 +561,8 @@ class MetricEstimator(Estimator):
def training(self, params):
x_fit, y_fit = data_mgr.metric('opt/' + params.train_file)
self.lr.fit(x_fit, y_fit)
y_predict = self.lr.predict(x_fit)
error_info(y_fit, y_predict, 'train')
print(self.lr.coef_)
def testing(self, params):
@@ -578,7 +580,7 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser(description='network training implementation')
# parser.add_argument('--train', default=True, type=bool, help='determine whether training the network')
parser.add_argument('--save', default=True, type=bool,
parser.add_argument('--save', default=False, type=bool,
help='determine whether saving the parameters of network, linear regression model, etc.')
parser.add_argument('--overwrite', default=False, type=bool,
help='determine whether overwriting the training and testing data')
@@ -588,7 +590,7 @@ if __name__ == '__main__':
parser.add_argument('--batch_size', default=2000, type=int, help='size of training batch')
parser.add_argument('--lr', default=1e-5, type=float, help='learning rate for the network')
parser.add_argument('--model', default='neural-network', help='method for assembly time estimation')
parser.add_argument('--machine_optimizer', default='feeder-scan', type=str, help='optimizer for single machine')
parser.add_argument('--machine_optimizer', default='feeder-priority', type=str, help='optimizer for single machine')
params = parser.parse_args()
data_mgr = DataMgr()
@@ -606,13 +608,13 @@ if __name__ == '__main__':
# data_mgr.remover() # remove the last saved data
# data_mgr.saver('data/' + file_name, pcb_data) # save new data
info = base_optimizer(1, pcb_data, component_data, pd.DataFrame(columns=['slot', 'part', 'arg']),
params, hinter=True)
info = base_optimizer(1, pcb_data, component_data, pd.DataFrame(columns=['slot', 'part']), params,
hinter=True)
data_mgr.recorder(f, info, pcb_data, component_data)
f.close()
estimator = MetricEstimator()
estimator = NeuralEstimator()
estimator.training(params)
estimator.testing(params)