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

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

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.

Suggested Citation

  • Chen, Mu-Chen & Hsiao, Yu-Hsiang & Himadeep Reddy, Reddivari & Tiwari, Manoj Kumar, 2016. "The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 208-226.
    • Handle: RePEc:eee:transe:v:91:y:2016:i:c:p:208-226
    DOI: 10.1016/j.tre.2016.04.003
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    References listed on IDEAS

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    Cited by:

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    5. Samuel Reong & Hui-Ming Wee & Yu-Lin Hsiao, 2022. "20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis," Mathematics, MDPI, vol. 10(19), pages 1-19, October.

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