Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2020

Abstract

Cross-docking is a logistics strategy that aims at less transportation costs and fast customer deliveries. Incorporating an efficient vehicle routing could increase the benefits of the cross-docking. In this paper, the vehicle routing problem with reverse cross-docking (VRP-RCD) is studied. Reverse logistics has attracted more attention due to its ability to gain more profit and maintain the competitiveness of a company. VRP-RCD includes a four-level supply chain network: suppliers, cross-dock, customers, and outlets, with the objective of minimizing vehicle operational and transportation costs. A two-phase heuristic that employs an adaptive large neighborhood search (ALNS) with various destroy and repair operators is proposed to solve benchmark instances. The simulated annealing framework is embedded to discover a vast search space during the search process. Experimental results show that our proposed ALNS obtains optimal solutions for 24 out of 30 problems of the first set of benchmark instances while getting better results for all instances in the second set of benchmark instances compared to optimization software.

Keywords

Vehicle routing problem, Cross-docking, Reverse logistics, Adaptive large neighborhood search

Discipline

Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 11th International Conference on Computational Logistics (ICCL 2020): September 28–30, Enschede, The Netherlands

Identifier

10.1007/978-3-030-59747-4_11

Publisher

Springer

City or Country

Germany

Comments

The conference paper is published in Lecture Notes in Computer Science 12433, pp. 167-182, Springer-Verlag Berlin Heidelberg, 2020 The full proceeding can be found here: https://link.springer.com/book/10.1007/978-3-030-59747-4

Additional URL

https://doi.org/10.1007/978-3-030-59747-4_11

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