The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Mu-Chen Chen*, Yu Hsiang Hsiao, Reddivari Himadeep Reddy, Manoj Kumar Tiwari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.

Original languageEnglish
Pages (from-to)208-226
Number of pages19
JournalTransportation Research Part E: Logistics and Transportation Review
Volume91
DOIs
StatePublished - 1 Jul 2016

Keywords

  • Genetic Algorithms
  • Multiple cross-docks
  • Particle Swarm Optimization
  • Self-learning strategy
  • Vehicle routing problem

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