Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale? (Papers Track)

Stephen Mak (University of Cambridge); Tim Pearce (Microsoft Research); Matthew Macfarlane (University of Amsterdam); Liming Xu (University of Cambridge); Michael Ostroumov (Value Chain Lab); Alexandra Brintrup (University of Cambridge)

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Transportation Reinforcement Learning


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-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.