Route planning for last-mile deliveries using mobile parcel lockers: a hybrid q-learning network approach
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Mobile parcel lockers (MPLs) have been recently proposed by logistics operators as a technology that could help reduce traffic congestion and operational costs in urban freight distribution. Given their ability to relocate throughout their area of deployment, they hold the potential to improve customer accessibility and convenience. In this study, we formulate the Mobile Parcel Locker Problem (MPLP), a special case of the Location-Routing Problem (LRP) which determines the optimal stopover location for MPLs throughout the day and plans corresponding delivery routes. A Hybrid Q-Learning-Network-based Method (HQM) is developed to resolve the computational complexity of the resulting large problem instances while escaping local optima. In addition, the HQM is integrated with global and local search mechanisms to resolve the dilemma of exploration and exploitation faced by classic reinforcement learning (RL) methods. We examine the performance of HQM under different problem sizes (up to 200 nodes) and benchmarked it against the exact approach and Genetic Algorithm (GA). Our results indicate that HQM achieves better optimisation performance with shorter computation time than the exact approach solved by the Gurobi solver in large problem instances. Additionally, the average reward obtained by HQM is 1.96 times greater than GA’s, which demonstrates that HQM has a better optimisation ability. Further, we identify critical factors that contribute to fleet size requirements, travel distances, and service delays. Our findings outline that the efficiency of MPLs is mainly contingent on the length of time windows and the deployment of MPL stopovers. Finally, we highlight managerial implications based on parametric analysis to provide guidance for logistics operators in the context of efficient last-mile distribution operations.
Date Issued
2023-09
Date Acceptance
2023-07-20
Citation
Transportation Research Part E: Logistics and Transportation Review, 2023, 177, pp.1-40
ISSN
1366-5545
Publisher
Elsevier
Start Page
1
End Page
40
Journal / Book Title
Transportation Research Part E: Logistics and Transportation Review
Volume
177
Copyright Statement
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifier
https://www.sciencedirect.com/science/article/pii/S1366554523002223
Publication Status
Published
Article Number
103234
Date Publish Online
2023-08-05