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Vehicle routing optimization with soft time windows in a fuzzy random environment

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  • Xu, Jiuping
  • Yan, Fang
  • Li, Steven

Abstract

This paper is concerned with a vehicle routing problem with soft time windows (VRPSTW) in a fuzzy random environment. Two objectives are considered: (1) minimize the total travel cost and (2) maximize the average satisfaction level of all customers. After setting up the model for the VRPSTW in a fuzzy random environment, the fuzzy random expected value concept is used to deal with the constraints and its equivalent crisp model is derived. The global–local–neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) is employed to solve the equivalent crisp model. A case study is also presented to illustrate the effectiveness of the proposed approach.

Suggested Citation

  • Xu, Jiuping & Yan, Fang & Li, Steven, 2011. "Vehicle routing optimization with soft time windows in a fuzzy random environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1075-1091.
    • Handle: RePEc:eee:transe:v:47:y:2011:i:6:p:1075-1091
    DOI: 10.1016/j.tre.2011.04.002
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    References listed on IDEAS

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    Cited by:

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    2. Taiwo Adetiloye, 2021. "Collaboration Planning of Stakeholders for Sustainable City Logistics Operations," Papers 2107.14049, arXiv.org.
    3. Themistoklis Stamadianos & Andromachi Taxidou & Magdalene Marinaki & Yannis Marinakis, 2024. "Swarm intelligence and nature inspired algorithms for solving vehicle routing problems: a survey," Operational Research, Springer, vol. 24(3), pages 1-45, September.
    4. Nourinejad, Mehdi & Roorda, Matthew J., 2014. "A dynamic carsharing decision support system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 66(C), pages 36-50.
    5. Allahviranloo, Mahdieh & Chow, Joseph Y.J. & Recker, Will W., 2014. "Selective vehicle routing problems under uncertainty without recourse," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 68-88.
    6. 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.
    7. Yan Sun & Xinya Li, 2019. "Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncerta," Mathematics, MDPI, vol. 7(8), pages 1-40, August.
    8. D. G. Mogale & Sri Krishna Kumar & Manoj Kumar Tiwari, 2020. "Green food supply chain design considering risk and post-harvest losses: a case study," Annals of Operations Research, Springer, vol. 295(1), pages 257-284, December.
    9. Subrat Sarangi & Sudipta Sarangi & Nasim S. Sabounchi, 2023. "How managerial perspectives affect the optimal fleet size and mix model: a multi-objective approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 1-23, March.
    10. Wanke, Peter & Barros, C.P. & Nwaogbe, Obioma R., 2016. "Assessing productive efficiency in Nigerian airports using Fuzzy-DEA," Transport Policy, Elsevier, vol. 49(C), pages 9-19.

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