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Optimization of Joint Distribution Routes for Automotive Parts Considering Multi-Manufacturer Collaboration

Author

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  • Lingsan Dong

    (School of Management, Harbin Institute of Technology, Harbin 150001, China)

  • Jian Wang

    (School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Xiaowei Hu

    (School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

Abstract

The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field.

Suggested Citation

  • Lingsan Dong & Jian Wang & Xiaowei Hu, 2025. "Optimization of Joint Distribution Routes for Automotive Parts Considering Multi-Manufacturer Collaboration," Sustainability, MDPI, vol. 17(14), pages 1-30, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:14:p:6615-:d:1705542
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    1. Desrosiers, Jacques & Soumis, Francois & Desrochers, Martin & SauveGerad, Michel, 1986. "Methods for routing with time windows," European Journal of Operational Research, Elsevier, vol. 23(2), pages 236-245, February.
    2. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    3. Alejandro Arenas-Vasco & Juan Carlos Rivera & Maria Gulnara Baldoquín, 2024. "Effect of formulations over a Periodic Capacitated Vehicle Routing Problem with multiple depots, heterogeneous fleet, and hard time-windows," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-19, October.
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