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Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Author

Listed:
  • Xiaodan Wu

    (Hebei University of Technology)

  • Ruichang Li

    (Hebei University of Technology)

  • Chao-Hsien Chu

    (Singapore Management University)

  • Richard Amoasi

    (Hebei University of Technology)

  • Shan Liu

    (Hebei University of Technology)

Abstract

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

Suggested Citation

  • Xiaodan Wu & Ruichang Li & Chao-Hsien Chu & Richard Amoasi & Shan Liu, 2022. "Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach," Annals of Operations Research, Springer, vol. 308(1), pages 653-684, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-021-04012-4
    DOI: 10.1007/s10479-021-04012-4
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    References listed on IDEAS

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    1. Baozhen Yao & Bin Yu & Ping Hu & Junjie Gao & Mingheng Zhang, 2016. "An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot," Annals of Operations Research, Springer, vol. 242(2), pages 303-320, July.
    2. Li, Xiangyong & Tian, Peng & Aneja, Y.P., 2010. "An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 1111-1127, November.
    3. Kramer, Raphael & Cordeau, Jean-François & Iori, Manuel, 2019. "Rich vehicle routing with auxiliary depots and anticipated deliveries: An application to pharmaceutical distribution," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 162-174.
    4. Yu, Yang & Wang, Sihan & Wang, Junwei & Huang, Min, 2019. "A branch-and-price algorithm for the heterogeneous fleet green vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 511-527.
    5. G. A. Croes, 1958. "A Method for Solving Traveling-Salesman Problems," Operations Research, INFORMS, vol. 6(6), pages 791-812, December.
    6. Marinakis, Yannis & Migdalas, Athanasios & Sifaleras, Angelo, 2017. "A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems," European Journal of Operational Research, Elsevier, vol. 261(3), pages 819-834.
    7. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "Thirty years of heterogeneous vehicle routing," European Journal of Operational Research, Elsevier, vol. 249(1), pages 1-21.
    8. Shiva Zandkarimkhani & Hassan Mina & Mehdi Biuki & Kannan Govindan, 2020. "A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design," Annals of Operations Research, Springer, vol. 295(1), pages 425-452, December.
    9. Tarantilis, C. D. & Kiranoudis, C. T. & Vassiliadis, V. S., 2004. "A threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 152(1), pages 148-158, January.
    10. Tino Henke & M. Grazia Speranza & Gerhard Wäscher, 2019. "A branch-and-cut algorithm for the multi-compartment vehicle routing problem with flexible compartment sizes," Annals of Operations Research, Springer, vol. 275(2), pages 321-338, April.
    11. Liu, Shuguang, 2013. "A hybrid population heuristic for the heterogeneous vehicle routing problems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 54(C), pages 67-78.
    12. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
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    2. Appiah, Michael & Li, Mingxing & Sehrish, Saba & Abaji, Emad Eddin, 2023. "Investigating the connections between innovation, natural resource extraction, and environmental pollution in OECD nations; examining the role of capital formation," Resources Policy, Elsevier, vol. 81(C).

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