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Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions

Author

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  • Mengke Li

    (College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Yongkui Shi

    (College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Meiyan Li

    (College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

Suggested Citation

  • Mengke Li & Yongkui Shi & Meiyan Li, 2023. "Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1659-:d:1111975
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    References listed on IDEAS

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    Cited by:

    1. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(C).

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