2D-Ptr

Source code for paper "2D-Ptr: 2D Array Pointer Network for Solving the Heterogeneous Capacitated Vehicle Routing Problem"

Dependencies

• Python>=3.8
• NumPy
• SciPy
• PyTorch>=1.12.1
• tqdm
• tensorboard_logger

Quick start

The implementation of the 2D-Ptr model is mainly in the file ./nets/attention_model.py

For testing HCVRP instances with 60 customers and 5 vehicles (V5-U60) and using pre-trained model:

# greedy
python eval.py data/hcvrp/hcvrp_v5_60_seed24610.pkl --model outputs/hcvrp_v5_60 --obj min-max --decode_strategy greedy --eval_batch_size 1
# sample1280
python eval.py data/hcvrp/hcvrp_v5_60_seed24610.pkl --model outputs/hcvrp_v5_60 --obj min-max --decode_strategy sample --width 1280 --eval_batch_size 1
# sample12800
python eval.py data/hcvrp/hcvrp_v5_60_seed24610.pkl --model outputs/hcvrp_v5_60 --obj min-max --decode_strategy sample --width 12800 --eval_batch_size 1

Since AAMAS limits the submission file size within 25Mb, we can only provide the pre-trained model on V5-U60 to avoid exceeding the limit.

PS: All pre-trained models have been uploaded!

Usage

Generating data

We have provided all the well-generated test datasets in ./data, and you can also generate each test set by:

python generate_data.py --dataset_size 1280 --veh_num 3 --graph_size 40

• The --graph_size and --veh_num represent the number of customers , vehicles and generated instances, respectively.

• The default random seed is 24610, and you can change it in ./generate_data.py.

• The test set will be stored in ./data/hcvrp/

Training

For training HCVRP instances with 40 customers and 3 vehicles (V3-U40):

python run.py --graph_size 40 --veh_num 3 --baseline rollout --run_name hcvrp_v3_40_rollout --obj min-max

• --run_name will be automatically appended with a timestamp, as the unique subpath for logs and checkpoints.
• The log based on Tensorboard will be stored in ./log/, and the checkpoint (or the well-trained model) will be stored in ./outputs/
• --obj represents the objective function, supporting min-max and min-sum

By default, training will happen on all available GPUs. Change the code in ./run.py to only use specific GPUs:

if __name__ == "__main__":
    warnings.filterwarnings('ignore')
    # os.environ["CUDA_VISIBLE_DEVICES"] = "0"
    run(get_options())

Evaluation

you can test a well-trained model on HCVRP instances with any problem size:

# greedy
python eval.py data/hcvrp/hcvrp_v3_40_seed24610.pkl --model outputs/hcvrp_v3_40 --obj min-max --decode_strategy greedy --eval_batch_size 1
# sample1280
python eval.py data/hcvrp/hcvrp_v3_40_seed24610.pkl --model outputs/hcvrp_v3_40 --obj min-max --decode_strategy sample --width 1280 --eval_batch_size 1
# sample12800
python eval.py data/hcvrp/hcvrp_v3_40_seed24610.pkl --model outputs/hcvrp_v3_40 --obj min-max --decode_strategy sample --width 12800 --eval_batch_size 1

• The --model represents the directory where the used model is located.
• The $filename$.pkl represents the test set.
• The --width represents sampling number, which is only available when --decode_strategy is sample.
• The --eval_batch_size is set to 1 for serial evaluation.