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What can i do with dataset like tsp with time windown thks

May I ask if the number of machines in each stage of the FFSP problem needs to be consistent? If it can be a heterogeneous quantity, how should pomo_size be set?

I have a question about job scheduling problem. My my problem is very complicated not like job shop so what if i change the problems define as meta heuristic algorithms as sample for model ?

In your paper, you mentioned that for n=50/100 large scale data, you adopted a multi-GPU implementation, I wonder if it is possible to provide a multi-GPU version of the code

Sorry because i dont understand the code and I need your help ! I have two question

• The first question: I understand that the score and aug_score is the tour length. But went i test it with my own dataset, i change the scale so the ans is different. So i want to ask that the train scale and test scale must be the same ?
• The second question: I know that max_pomo_reward.max(dim=0) is the best tour and I dont know how to get it, and I know self.env.slected_node_list is the tour but how i can get it from the best tour ?
  TKS!!

Hi, it seems that the trained model saved in results/saved_atsp_model is not available now. I tried to get the model from git lfs, but it shows the model does not exist on the server. Could you please solve this problem? Thanks very much.

How I modify the code for symmetric TSP

Have you ever tried to solve the dynamic vehicle scheduling problem with this model？

If I met the Multiple data matrices, what should I do， May I do like this ： scores = torch.stack((dot_product_score, cost_mat1, cost_mat2, cost_mat3 ), dim=4).
There may also be papers that I didn't fully understand, forgive me for being a newbie

max_aug_pomo_reward, _ = max_pomo_reward.max(dim=0) # get best results from augmentation
# shape: (batch,)
aug_score = -max_aug_pomo_reward.float().mean() # negative sign to make positive value

I want to ask why you use .mean() but we want to get the best result