Learning to optimize the dispatch time interval for on-demand food delivery service
Publication Type
Conference Proceeding Article
Publication Date
12-2024
Abstract
In recent years, the rapid advancement of mobile and wireless communication technologies has enabled real-time connectivity for on-demand delivery platforms, facilitating efficient door-to-door services like online food delivery. This study addresses a practical challenge faced by a food delivery platform, where customer orders must be allocated to drivers responsible for collecting food from designated centers and delivering it to customers within specific time windows. This dynamic pickup and delivery problem emphasizes prompt delivery as the critical objective. Our research focuses on optimizing the dispatch intervals for orders on such platforms. We tackle this by formulating the problem as a Markov decision process (MDP) and introducing a two-stage framework that combines a multi-agent reinforcement learning (RL) approach for order dispatching with a heuristic method for driver routing. The RL algorithm determines the optimal timing for each order's entry into the matching pool, while the routing method integrates orders into drivers' delivery routes. Extensive experiments, using real-world data and a simulator, show our results surpass benchmark methods, enhancing the efficiency of order dispatching in on-demand food delivery services.
Keywords
Intelligent Logistics, Simulation and Modeling, Other Theories, Applications, Technologies
Discipline
Artificial Intelligence and Robotics | Transportation
Publication
Proceedings of the 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, Canada, September 24- 27
City or Country
Edmonton, Canada
Citation
YANG, Jingfeng; ZHANG, Zhiqin; and LAU, Hoong Chuin.
Learning to optimize the dispatch time interval for on-demand food delivery service. (2024). Proceedings of the 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Edmonton, Canada, September 24- 27.
Available at: https://ink.library.smu.edu.sg/sis_research/10089
Additional URL
https://its.papercept.net/conferences/scripts/abstract.pl?ConfID=87&Number=700
