@inproceedings{
mak2023cooperative,
title={Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?},
author={Stephen Mak and Tim Pearce and Matthew Macfarlane and Liming Xu and Michael Ostroumov and Alexandra Brintrup},
booktitle={NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory to Deployment},
year={2023},
url={https://openreview.net/forum?id=7T4YMOj7MS}
}
Cooperative Logistics studies the setting where logistics companies pool their resources together to improve their individual performance. Prior literature suggests carbon savings of approximately 22%. If attained globally, this equates to 480,000,000 tonnes of CO2. Whilst well-studied in operations research – industrial adoption remains limited due to a lack of trustworthy cooperation. A key remaining challenge is fair and scalable gain sharing (i.e., how much should each company be fairly paid?). This paper introduces the novel algorithmic challenges that Cooperative Logistics offers AI, and novel applications of AI towards Cooperative Logistics. We further present ﬁndings from our initial experiments.
Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?
Stephen Mak Tim Pearce Matthew Macfarlane Liming Xu Michael Ostroumov Alexandra Brintrup