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Particle swarm optimization for the multi-period cross-docking distribution problem with time windows

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  • Vincent F. Yu
  • Parida Jewpanya
  • Voratas Kachitvichyanukul

Abstract

Cross-docking has emerged as a new technique in supply chain management to replace the warehouse concept in the retail industry. This paper proposes a multi-period cross-docking distribution problem that consists of manufacturers, cross-docks and customers. This model is formulated for cases that consider multiple products, consolidation of customer orders and time windows that are available in multiple periods. The objective function is to minimise the total cost, which includes transportation cost, inventory cost and penalty cost. The penalty cost arises when demand remains in each period that cannot be satisfied. To deal with the complexity of the problem, an algorithm is developed based on particle swarm optimisation (PSO) with multiple social learning terms, GLNPSO, with two solution representations. The solution representations are a one-period solution representation (OP-SR) and a multi-period solution representation (MP-SR). The GLNPSO-based algorithm performs well in solving this problem. Moreover, both representations are proven effective when comparing the solution quality and computational time with those results obtained from CPLEX. In terms of quality, the MP-SR solution is better than the OP-SR solution for both stable and fluctuating demand instances. However, MP-SR requires more computational effort than OP-SR.

Suggested Citation

  • Vincent F. Yu & Parida Jewpanya & Voratas Kachitvichyanukul, 2016. "Particle swarm optimization for the multi-period cross-docking distribution problem with time windows," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 509-525, January.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:2:p:509-525
    DOI: 10.1080/00207543.2015.1037933
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    References listed on IDEAS

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    Cited by:

    1. Saeid Nasrollahi & Hasan Hosseini-Nasab & Mohamad Bagher Fakhrzad & Mahboobeh Honarvar, 2022. "A developed nonlinear model for the location-allocation and transportation problems in a cross-docking distribution network," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 127-148.
    2. Yi Zhang & Guowei Hua & T. C. E. Cheng & Juliang Zhang, 2020. "Cold chain distribution: How to deal with node and arc time windows?," Annals of Operations Research, Springer, vol. 291(1), pages 1127-1151, August.

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