Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2020
Abstract
This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD. The aim is to find a set of routes to deliver single products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic approach that uses the routes of the local optima of an adaptive large neighborhood search (ALNS) as columns in a set-partitioning formulation of the VRPCD is designed. This matheuristic outperforms the state-of-the-art algorithms in solving a subset of benchmark instances.
Keywords
Vehicle routing problem, Cross-docking, Scheduling, Matheuristic
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 14th Learning and Intelligent OptimizatioN Conference (LION 2020), Athens, Greece, May 24-28
First Page
9
Last Page
15
ISBN
9783030535513
Identifier
10.1007/978-3-030-53552-0_2
Publisher
Springer
City or Country
Greece
Citation
GUNAWAN, Aldy; WIDJAJA, Audrey Tedja; VANSTEENWEGEN, Pieter; and YU, Vincent F..
A matheuristic algorithm for solving the vehicle routing problem with cross-docking. (2020). Proceedings of the 14th Learning and Intelligent OptimizatioN Conference (LION 2020), Athens, Greece, May 24-28. 9-15.
Available at: https://ink.library.smu.edu.sg/sis_research/5265
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/10.1007/978-3-030-53552-0_2