A hybrid pricing and cutting approach for the multi-shift full truckload vehicle routing problem

Ning XUE, Ruibin Bai, Rong Qu, Uwe Aickelin

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)500-514
Number of pages15
JournalEuropean Journal of Operational Research
Volume292
Issue number2
DOIs
Publication statusPublished - 16 Jul 2021

Keywords

  • Column generation
  • Full truckload transport
  • Metaheuristics
  • Pricing and cutting
  • Transportation

ASJC Scopus subject areas

  • Computer Science (all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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