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Dynamic joint construction and optimal operation strategy of multi-period reverse logistics network: a case study of Shanghai apparel E-commerce enterprises

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Listed:
  • Jianquan Guo

    (Business School of University of Shanghai for Science and Technology)

  • Xinxin Liu

    (Business School of University of Shanghai for Science and Technology)

  • Jungbok Jo

    (Dongseo University)

Abstract

With the rapid development of our economy, the demand for reverse logistics is becoming increasingly urgent. This paper proposes a multi-period and dynamic joint construction model to build the reverse logistics network and verifies the feasibility of the rendered model by adopting particle swarm optimization (PSO) algorithm and genetic algorithm (GA) with a case study of the apparel E-commerce enterprises of Shanghai. The calculation indicates that, compared with traditional single-period reverse logistics system, dynamic joint construction model is more accordant with the practical situation, the capacity of multi-period model node is easier to optimize and the total operation cost of multi-period model reduces a lot. Furthermore, this research provides reference for reducing the operation costs as well as the building of regional reverse logistics network.

Suggested Citation

  • Jianquan Guo & Xinxin Liu & Jungbok Jo, 2017. "Dynamic joint construction and optimal operation strategy of multi-period reverse logistics network: a case study of Shanghai apparel E-commerce enterprises," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 819-831, March.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:3:d:10.1007_s10845-015-1034-8
    DOI: 10.1007/s10845-015-1034-8
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    References listed on IDEAS

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    1. Soleimani, Hamed & Govindan, Kannan, 2014. "Reverse logistics network design and planning utilizing conditional value at risk," European Journal of Operational Research, Elsevier, vol. 237(2), pages 487-497.
    2. Alumur, Sibel A. & Nickel, Stefan & Saldanha-da-Gama, Francisco & Verter, Vedat, 2012. "Multi-period reverse logistics network design," European Journal of Operational Research, Elsevier, vol. 220(1), pages 67-78.
    3. Rahman, Shams & Subramanian, Nachiappan, 2012. "Factors for implementing end-of-life computer recycling operations in reverse supply chains," International Journal of Production Economics, Elsevier, vol. 140(1), pages 239-248.
    4. Kara, S. & Rugrungruang, F. & Kaebernick, H., 2007. "Simulation modelling of reverse logistics networks," International Journal of Production Economics, Elsevier, vol. 106(1), pages 61-69, March.
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    Cited by:

    1. Navid Zarbakhshnia & Devika Kannan & Reza Kiani Mavi & Hamed Soleimani, 2020. "A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty," Annals of Operations Research, Springer, vol. 295(2), pages 843-880, December.
    2. Fernando Tobal Berssaneti & Simone Berger & Ana Maria Saut & Rosangela Maria Vanalle & José Carlos Curvelo Santana, 2019. "Value Generation of Remanufactured Products: Multi-Case Study of Third-Party Companies," Sustainability, MDPI, vol. 11(3), pages 1-21, January.

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