Enabling sustainable freight forwarding network via collaborative games
Publication Type
Conference Proceeding Article
Publication Date
8-2024
Abstract
Freight forwarding plays a crucial role in facilitating global trade and logistics. However, as the freight forwarding market is extremely fragmented, freight forwarders often face the issue of not being able to fill the available shipping capacity. This recurrent issue motivates the creation of various freight forwarding networks that aim at exchanging capacities and demands so that the resource utilization of individual freight forwarders can be maximized. In this paper, we focus on how to design such a collaborative network based on collaborative game theory, with the Shapley value representing a fair scheme for profit sharing. Noting that the exact computation of Shapley values is intractable for large-scale real-world scenarios, we incorporate the observation that collaboration among two forwarders is only possible if their service routes and demands overlap. This leads to a new class of collaborative games called the Locally Collaborative Games (LCGs), where agents can only collaborate with their neighbors. We propose an efficient approach to compute Shapley values for LCGs, and numerically demonstrate that our approach significantly outperforms the state-of-the-art approach for a wide variety of network structures.
Keywords
Game Theory and Economic Paradigms, cooperative games, transport
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Transportation
Publication
Proceedings of the 33rd International Joint Conference on Artificial Intelligence 2024: Jeju, August 3-9
First Page
2976
Last Page
2983
ISBN
9781956792041
Identifier
10.24963/ijcai.2024/330
Publisher
International Joint Conferences on Artificial Intelligence
City or Country
Jeju
Citation
TAN, Pang-Jin; CHENG, Shih-Fen; and CHEN, Richard.
Enabling sustainable freight forwarding network via collaborative games. (2024). Proceedings of the 33rd International Joint Conference on Artificial Intelligence 2024: Jeju, August 3-9. 2976-2983.
Available at: https://ink.library.smu.edu.sg/sis_research/9505
Additional URL
https://doi.org/10.24963/ijcai.2024/330