This repository comprises the code to learn a dispatching and routing policy for the Vehicle Routing Problem with Time Windows using a structured learning enriched combinatorial optimization pipeline. The problem setting bases on the EURO Meets NeurIPS 2022 Vehicle Routing Competition

This method is proposed in:

Léo Baty, Kai Jungel, Patrick Klein, Axel Parmentier, and Maximilian Schiffer. Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows. arXiv preprint: arxiv:2304.00789, 2023.

This repository contains all relevant scripts and data sets to reproduce the results from the paper. To run the code for reproducing the results we assume using slurm.

The structure of this repository is as follows:
./evaluation: code to evaluate the learned ML-CO policy
./experiments: directory containing all anticipative lower bound target solutions for different experiments
./features: code to create features
./instances: directory containing all static instances
./pchgs: code to run PC-HGS
./training: code to train ML-CO policy

To reproduce the results from the paper follow these steps:

Building PC-HGS requires

• cmake >= 3.14
• A compiler with C++-20 support

1. Clone the repository and initialize submodules.

  git clone https://github.com/tumBAIS/euro-meets-neurips-2022.git
  cd pchgs

2. Generate a makefile

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE="RELEASE"

Support build types: RELEASE, DEBUG, RELWITHDEBINFO

3. Build

make

0. Install the dependencies

pip install -r requirements.txt

1. Define the training configuration in ./training/src/config.py
2. To start the training go to ./training/ and run

python run_training.py

3. To start the training in order to reproduce the results from the paper go to in ./training/ and run

bash master_experiments.sh

The evaluation of different benchmark policies and the ML-CO policy mainly bases on the code provided from the EURO Meets NeurIPS 2022 Vehicle Routing Competition.

1. Define the evaluation configuration in ./evaluation/src/config.py
2. To start evaluating policies go to ./evaluation/ and run

python solve_bound.py

3. To start the evaluation in order to reproduce the results from the paper go to ./evaluation/ and first identify the best learning iteration for each trained model by running

bash master_validation.sh 

Save the best found learning iteration for each model in ./evaluation/src/read_in_learning_iteration.txt. Second, to start the final test run, go to ./evaluation/ and run

bash master_test.sh