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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

Cooperative Logistics: Can AI Enable Trustworthy Cooperation at Scale?

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


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 440,000,000 tonnes of CO2-eq. 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?). We propose the use of deep reinforcement learning with a neural reward model for coalition structure generation and present early findings.

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