A Decomposition-Based Hybrid Algorithm for Multi-objective Vehicle Routing Problem with Time Windows

Yiwen Wang, Fei Liu, Qingfu Zhang

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Citations (Scopus)

Abstract

The Vehicle routing problems (VRP) are one of the most studied combinatorial optimization problems. This paper targets an important and challenging VRP variant, named multi-objective vehicle routing problems with time windows. We propose to use a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to decompose the problem into a set of single-objective sub-problems. For each sub-problem, efficient crossover and local search heuristics are adopted to generate and improve new solutions. We design two new strategies to decrease the number of vehicles, a population management method with infeasible solutions and a multi-split strategy. Experimental studies are carried out on the well-known Solomon's dataset. Results suggest that our proposed algorithm is very competitive compared to two state-of-the-art algorithms. It generates better solutions on 90% of the test instances. © 2023 IEEE.
Original languageEnglish
Title of host publication2023 IEEE Congress on Evolutionary Computation (CEC)
PublisherIEEE
ISBN (Electronic)979-8-3503-1458-8
ISBN (Print)979-8-3503-1459-5
DOIs
Publication statusPublished - 2023
Event2023 IEEE Congress on Evolutionary Computation (CEC 2023) - Swissôtel Hotels & Resorts, Chicago, United States
Duration: 1 Jul 20235 Jul 2023

Publication series

NameIEEE Congress on Evolutionary Computation, CEC

Conference

Conference2023 IEEE Congress on Evolutionary Computation (CEC 2023)
Country/TerritoryUnited States
CityChicago
Period1/07/235/07/23

Research Keywords

  • Vehicle routing problem with time windows
  • MOEA/D
  • Multi-objective optimization
  • Hybrid genetic search
  • Local search

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