This repo is the official implementation of Electric Vehicle Routing for Emergency Power Supply with Deep Reinforcement Learning (AAMAS 2024, extended abstract) and Electric Vehicle Routing for Emergency Power Supply: Towards Telecom Base Station Relief (arXiv preprint).
evrp-anim.mp4
We recommend using Docker to construct the python environment. You can use the Dockerfile in this repository.
docker build -t evrp-eps/evrp-eps:1.0 .
You can run code interactively with the following command (<> indicates a placeholder, which you should replace according to your settings.").
docker run -it --rm -v </path/to/clone/repo>:/workspace/app --name evrp-eps -p <host_port>:<container_port> --gpus all evrp-eps/evrp-eps:1.0 bash
First of all, we generate synthetic datasets for training/validation/evaluation. We recommend more than 12.8M training samples.
If you want to change some parameters, check the other options with python generate_datasets.py -h.
python generate_dataset.py --save_dir data/synthetic_data --type all --num_samples 1280000 10000 10000
We train the RL model on the synthetic datasets. Check the other options with python train.py -h.
python train.py --dataset_path data/synthetic_data/train_dataset.pkl --checkpoint_dir checkpoints/demo_model --batch_size 256 --vehicle_speed 41 --wait_time 0.5 --time_horizon 12 --gpu 0
We determine the best epoch evaluating the RL-model with greedy decoding on the validation split. Check the other options with python valid.py -h.
python valid.py --model_dir checkpoints/demo_model --dataset_path data/synthetic_data/valid_dataset.pkl --gpu 0
The option --model_dir <check_point_dir> automatically selects the weights at the best epoch. You can also select a specific epoch by --model_path model_epoch<epoch>.pth. Specify only one of the two. If you want to output the visualization, add the option --visualize_routes. Check the other options with python eval.py -h.
python eval.py --model_dir checkpoints/demo_model --dataset_path data/synthetic_data/eval_dataset.pkl --vehicle_speed 41 --wait_time 0.5 --time_horizon 12 --gpu 0
Regarding the synthetic datasets, you can reproduce our experimental results in reproduce_results.ipynb.
Please take a glance at the content via nbviwer: nbviwer-evrp-eps.
You can open the Jupyter Notebook server with the following command inside the container, then access it from your browser on localhost.
jupyter lab --allow-root --no-browser --ip=0.0.0.0 --port <container_port>
If you encounter a bug or have any questions, please post issues in this repo.
Our code is licenced by NTT. Basically, the use of our code is limitted to research purposes. See LICENSE for more details.
If you find this work useful, please cite our paper as follows:
@misc{kikuta2024electric,
title={Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station Relief},
author={Daisuke Kikuta and Hiroki Ikeuchi and Kengo Tajiri and Yuta Toyama and Masaki Nakamura and Yuusuke Nakano},
year={2024},
eprint={2404.02448},
archivePrefix={arXiv},
primaryClass={math.OC}
}