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Stochastic Multi-Objective Multi-Trip AMR Routing Problem with Time Windows

Author

Listed:
  • Lulu Cheng

    (Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China)

  • Ning Zhao

    (Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China)

  • Kan Wu

    (Business Analytics Research Center, Chang Gung University, Taoyuan City 33302, Taiwan)

Abstract

In recent years, with the rapidly aging population, alleviating the pressure on medical staff has become a critical issue. To improve the work efficiency of medical staff and reduce the risk of infection, we consider the multi-trip autonomous mobile robot (AMR) routing problem in a stochastic environment. Our goal is to minimize the total expected operating cost and maximize the total service quality for patients, ensuring that each route violates the vehicle capacity and the time window with only a minimal probability. The travel time of AMRs is stochastically affected by the surrounding environment; the demand for each ward is unknown until the AMR reaches the ward, and the service time is linearly related to the actual demand. We developed a population-based tabu search algorithm (PTS) that combines the genetic algorithm with the tabu search algorithm to solve this problem. Extensive numerical experiments were conducted on the modified Solomon instances to demonstrate the efficiency of the PTS algorithm and reveal the impacts of the confidence level on the optimal solution, providing insights for decision-makers to devise delivery schemes that balance operating costs with patient satisfaction.

Suggested Citation

  • Lulu Cheng & Ning Zhao & Kan Wu, 2024. "Stochastic Multi-Objective Multi-Trip AMR Routing Problem with Time Windows," Mathematics, MDPI, vol. 12(15), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2394-:d:1447286
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    References listed on IDEAS

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    1. Tan, K.C. & Cheong, C.Y. & Goh, C.K., 2007. "Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation," European Journal of Operational Research, Elsevier, vol. 177(2), pages 813-839, March.
    2. Benjamin Biesinger & Bin Hu & Günther R. Raidl, 2018. "A Genetic Algorithm in Combination with a Solution Archive for Solving the Generalized Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 52(3), pages 673-690, June.
    3. Ehmke, Jan Fabian & Campbell, Ann Melissa & Urban, Timothy L., 2015. "Ensuring service levels in routing problems with time windows and stochastic travel times," European Journal of Operational Research, Elsevier, vol. 240(2), pages 539-550.
    4. Aziez, Imadeddine & Côté, Jean-François & Coelho, Leandro C., 2022. "Fleet sizing and routing of healthcare automated guided vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
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