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Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context

Dependencies

  • Python>=3.8
  • NumPy
  • PyTorch>=1.7
  • tqdm
  • tsplib95

Training

To train the model on MTSP and MPDP instances with 50 nodes and a variable number of agents ranging from 2 to 10:

python run.py --graph_size 50 --problem mtsp --run_name 'mtsp50' --agent_min 2 --agent_max 10
python run.py --graph_size 50 --problem mpdp --run_name 'mpdp50' --agent_min 2 --agent_max 10

Generating data

To generate validation and test data (same as used in the paper) for mtsp and mpdp problems

python generate_data.py --problem mtsp --graph_sizes 50 --dataset_size 100 --name test --seed 3333 
python generate_data.py --problem mpdp --graph_sizes 50 --dataset_size 100 --name test --seed 3333

Finetuning

You can finetuning using a pretrained model by using the --load_path option:

python run.py --graph_size 200 --problem mtsp --load_path pretrained/mtsp/mtsp50/epoch-100.pt --batch_size 64 --agent_min 10 --agent_max 20 --ft Y

Evalutation

Serialization

  • MTSP 
    python eval.py data/mtsp/mtsp200_test_seed3333.pkl --problem mtsp --model pretrained/mtsp/mtsp200/epoch-4.pt --decode_strategy greedy --agent_num 10  --is_serial True --val_size 100 --ft Y
  • MPDP 
    python eval.py data/mpdp/mpdp200_test_seed3333.pkl --problem pdp --model pretrained/mpdp/mpdp200/epoch-0.pt --decode_strategy greedy --agent_num 10  --is_serial True --val_size 100 --ft Y

Parallelization

You can adjust batch_size to --eval_batch_size argument if memory is insufficient when you run parrellel

  • MTSP 
    python eval.py data/mtsp/mtsp200_test_seed3333.pkl --problem mtsp --model pretrained/mtsp/mtsp200/epoch-4.pt --decode_strategy greedy --agent_num 10  --is_serial False --val_size 100 --eval_batch_size 25 --ft Y
  • MPDP 
    python eval.py data/mpdp/mpdp200_test_seed3333.pkl --problem pdp --model pretrained/mpdp/mpdp200/epoch-0.pt --decode_strategy greedy --agent_num 10 --eval_batch_size 25 --is_serial False --val_size 100 --ft Y