Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2022
Abstract
In this paper, we are concerned with the automated exchange of orders between logistics companies in a marketplace platform to optimize total revenues. We introduce a novel multi-agent approach to this problem, focusing on the Collaborative Vehicle Routing Problem (CVRP) through the lens of individual rationality. Our proposed algorithm applies the principles of Vehicle Routing Problem (VRP) to pairs of vehicles from different logistics companies, optimizing the overall routes while considering standard VRP constraints plus individual rationality constraints. By facilitating cooperation among competing logistics agents through a Give-and-Take approach, we show that it is possible to reduce travel distance and increase operational efficiency system-wide. More importantly, our approach ensures individual rationality and faster convergence, which are important properties of ensuring the long-term sustainability of the marketplace platform. We demonstrate the efficacy of our approach through extensive experiments using real-world test data from major logistics companies. The results reveal our algorithm's ability to rapidly identify numerous optimal solutions, underscoring its practical applicability and potential to transform the logistics industry.
Keywords
Collaborative Vehicle Routing Problem, Give-and-Take, Logistics
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Publication
In IJCAI 2023 Workshop on Search and Planning with Complex Objectives, Macao, China, 2023
Identifier
10.48550/arXiv.2308.16501
City or Country
California, USA
Citation
PHONG, Tran; TANG, Paul; and LAU, Hoong Chuin.
Individually rational collaborative vehicle routing through Give-and-Take exchanges. (2022). In IJCAI 2023 Workshop on Search and Planning with Complex Objectives, Macao, China, 2023.
Available at: https://ink.library.smu.edu.sg/sis_research/8311
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.48550/arXiv.2308.16501