This archive is distributed in association with the INFORMS Journal on Computing under the MIT License.
The purpose of this repository is to share the source code used in the paper "An Approximate Dynamic Programming Approach to Dynamic Stochastic Matching" by F. You and T. Vossen.
The code contains implementation of main model of the paper, benchmark approaches for comparision, numerical instance generation and experimentation.
To replicate numerical experiments from the paper, or use the code to solve new dynamic stochastic matching instances, python packages numpy, networkx, as well as the Gurobi solver and the gurobipy package are required.
To cite the contents of this repository, please cite both the paper and this repo, using their respective DOIs.
https://doi.org/10.1287/ijoc.2021.0203
https://doi.org/10.1287/ijoc.2021.0203.cd
Below is the BibTex for citing this version of the data.
@article{adp_matching_code,
title = {An Approximate Dynamic Programming Approach to Dynamic Stochastic Matching},
author = {F. You and T. Vossen},
year = {2023},
journal = {{INFORMS Journal on Computing}},
doi={10.1287/ijoc.2021.0203.cd},
note={available for download at https://github.com/INFORMSJoC/2021.0203}
}Synthetic instance generation and numerical simulation code is provided:
to recreate ridesharing results, run python src/ridesharing.py
to recreate matchmaking results, run python src/matchmaking.py
to recreate kidney exchange results, run python src/kidney.py
This software is released under the MIT license, which we report in file LICENSE.