Decomposition-based multi-objective evolutionary algorithm for vehicle routing problem with stochastic demands

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

12 Scopus Citations
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  • Sen Bong Gee
  • Willson Amalraj Arokiasami
  • Jing Jiang
  • Kay Chen Tan


Original languageEnglish
Pages (from-to)3443-3453
Journal / PublicationSoft Computing
Issue number9
Publication statusPublished - 1 Sep 2016
Externally publishedYes


Vehicle routing problem with stochastic demands (VRPSD) is a famous and challenging optimization problem which is similar to many real world problems. To resemble the real world scenario, total traveling distance, total driver remuneration, the number of vehicles used and the difference between driver remuneration are considered and formulated in the multi-objective optimization perspective. This paper aims to solve multi-objective VRPSD under the constraints of available time window and vehicle capacity using decomposition-based multi-objective evolutionary algorithm (MOEA/D) with diversity-loss-based selection method incorporates with local search and multi-mode mutation heuristics. We have also compared the optimization performance of the decomposition-based approach with the domination-based approach to study the difference between these two well-known evolutionary multi-objective algorithm frameworks. The simulation results have showed that the decomposition-based approach with diversity-loss-based selection method is able to maintain diverse output solutions.

Research Area(s)

  • Evolutionary algorithm, Multi-objective optimization, Vehicle routing problem

Citation Format(s)