Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?

Stephen Mak, Tim Pearce, Matthew Macfarlane, Liming Xu, Michael Ostroumov, Alexandra Brintrup

NeurIPSW 2023

/neuripsw/2023/mak2023neuripsw-cooperative/

Abstract

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.

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Cite

Text

Mak et al. "Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?." NeurIPS 2023 Workshops: CompSust, 2023.

Markdown

[Mak et al. "Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?." NeurIPS 2023 Workshops: CompSust, 2023.](https://mlanthology.org/neuripsw/2023/mak2023neuripsw-cooperative/)

BibTeX

@inproceedings{mak2023neuripsw-cooperative,
  title     = {{Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale?}},
  author    = {Mak, Stephen and Pearce, Tim and Macfarlane, Matthew and Xu, Liming and Ostroumov, Michael and Brintrup, Alexandra},
  booktitle = {NeurIPS 2023 Workshops: CompSust},
  year      = {2023},
  url       = {https://mlanthology.org/neuripsw/2023/mak2023neuripsw-cooperative/}
}