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Fuzzy Demand Vehicle Routing Problem with Soft Time Windows

Author

Listed:
  • Tao Yang

    (College of Extended Education, Chongqing University of Education, Chongqing 400067, China)

  • Weixin Wang

    (School of International Business and Management, Sichuan International Studies University, Chongqing 400031, China)

  • Qiqi Wu

    (College of Finance and Economics, Sichuan International Studies University, Chongqing 400031, China)

Abstract

Considering the vehicle routing problem with fuzzy demand and fuzzy time windows, a vehicle routing optimization method is proposed considering both soft time windows and uncertain customer demand. First, a fuzzy chance-constrained programming model is established based on credibility theory, minimizing the total logistics cost. At the same time, a random simulation algorithm is designed to calculate the penalty cost of delivery failures caused by demand that cannot be satisfied. In order to overcome the shortcomings of GA, which easily falls into the local optimum in the process of searching, and the slow convergence speed of SA when the population is too large, a hybrid simulated annealing–genetic algorithm is adopted to improve the solution quality and efficiency. Finally, the Solomon standard example is used to verify the effectiveness of the algorithm, and the influence of decision-makers’ subjective cost preference is analyzed.

Suggested Citation

  • Tao Yang & Weixin Wang & Qiqi Wu, 2022. "Fuzzy Demand Vehicle Routing Problem with Soft Time Windows," Sustainability, MDPI, vol. 14(9), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5658-:d:810703
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    References listed on IDEAS

    as
    1. Zheng Wang & Chunyue Zhou, 2016. "A Three-Stage Saving-Based Heuristic for Vehicle Routing Problem with Time Windows and Stochastic Travel Times," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-10, March.
    2. Nadizadeh, Ali & Hosseini Nasab, Hasan, 2014. "Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm," European Journal of Operational Research, Elsevier, vol. 238(2), pages 458-470.
    3. Maaike Hoogeboom & Wout Dullaert & David Lai & Daniele Vigo, 2020. "Efficient Neighborhood Evaluations for the Vehicle Routing Problem with Multiple Time Windows," Transportation Science, INFORMS, vol. 54(2), pages 400-416, March.
    4. Saber Shiripour & Nezam Mahdavi-Amiri & Iraj Mahdavi, 2017. "A nonlinear model for location-allocation-routing problem in transportation network with intelligent travel times," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 29(3), pages 400-431.
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    Cited by:

    1. Feng, Xuehao & Song, Rui & Yin, Wenwei & Yin, Xiaowei & Zhang, Ruiyou, 2023. "Multimodal transportation network with cargo containerization technology: Advantages and challenges," Transport Policy, Elsevier, vol. 132(C), pages 128-143.

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