A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem

Xue, Ning; Bai, Ruibin; Qu, Rong; Aickelin, Uwe

Abstract

Full truckload transportation (FTL) in the form of freight containers represents one of the most important transportation modes in international trade. Due to large volume and scale, in FTL, delivery time is often less critical but cost and service quality are crucial. Therefore, efficiently solving large scale multiple shift FTL problems is becoming more and more important and requires further research. In one of our earlier studies, a set covering model and a three-stage solution method were developed for a multi-shift FTL problem. This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm). The metaheuristics were adopted to find promising columns (vehicle routes) guided by pricing and cuts are dynamically generated to eliminate infeasible flow assignments caused by incompatible commodities. Computational experiments on real-life and artificial benchmark FTL problems showed superior performance both in terms of computational time and solution quality, when compared with previous MIP based three-stage methods and two existing metaheuristics. The proposed cutting and heuristic pricing approach can efficiently solve large scale real-life FTL problems.

Citation

Xue, N., Bai, R., Qu, R., & Aickelin, U. (2021). A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem. European Journal of Operational Research, 292(2), 500-514. https://doi.org/10.1016/j.ejor.2020.10.037

• Journal Article Type: Article
• Acceptance Date: Oct 27, 2020
• Online Publication Date: Oct 30, 2020
• Publication Date: Jul 16, 2021
• Deposit Date: Feb 28, 2025
• Publicly Available Date: Feb 28, 2025
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Electronic ISSN: 1872-6860
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 292
• Issue: 2
• Pages: 500-514
• DOI: https://doi.org/10.1016/j.ejor.2020.10.037
• Public URL: https://nottingham-repository.worktribe.com/output/44224153
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0377221720309139