Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2021

Abstract

The emergence of e-Commerce imposes a tremendous strain on urban logistics which in turn raises concerns on environmental sustainability if not performed efficiently. While large logistics service providers (LSPs) can perform fulfillment sustainably as they operate extensive logistic networks, last-mile logistics are typically performed by small LSPs who need to form alliances to reduce delivery costs and improve efficiency, and to compete with large players. In this paper, we consider a multi-alliance multi-depot pickup and delivery problem with time windows (MAD-PDPTW) and formulate it as a mixed-integer programming (MIP) model. To cope with large-scale problem instances, we propose a two-stage approach of deciding how LSP requests are distributed to alliances, followed by vehicle routing within each alliance. For the former, we propose machine learning models to learn the values of delivery costs from past delivery data, which serve as a surrogate for deciding how requests are assigned. For the latter, we propose a tabu search heuristic. Experimental results on a standard dataset and a real case in Singapore show that our proposed learning-based optimization framework is efficient and effective in outperforming the direct use of tabu search in most instances. Using our approach, we demonstrate that substantial savings in costs and hence improvement in sustainability can be achieved when these LSPs form alliances and requests are optimally assigned to these alliances.

Keywords

Alliances, Collaboration, Machine Learning, Pickup-and-delivery, Tabu Search

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Computational Logistics: ICCL 2021, September 27-29, Virtual: Proceedings

Volume

13004

First Page

316

Last Page

331

ISBN

9783030876722

Identifier

10.1007/978-3-030-87672-2_21

Publisher

Springer

City or Country

Cham

Embargo Period

11-2-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-030-87672-2_21

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