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Robust Optimization of Fourth Party Logistics Network Design under Disruptions

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  • Jia Li
  • Yanqiu Liu
  • Ying Zhang
  • Zhongjun Hu

Abstract

The Fourth Party Logistics (4PL) network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β -robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA) and the genetic algorithm (GA) are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.

Suggested Citation

  • Jia Li & Yanqiu Liu & Ying Zhang & Zhongjun Hu, 2015. "Robust Optimization of Fourth Party Logistics Network Design under Disruptions," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-7, February.
    • Handle: RePEc:hin:jnddns:720628
    DOI: 10.1155/2015/720628
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    Cited by:

    1. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).

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