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Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Author

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  • Yong Wang

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Qin Li

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Xiangyang Guan

    (Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA)

  • Jianxin Fan

    (School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China)

  • Yong Liu

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China)

  • Haizhong Wang

    (School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97330, USA)

Abstract

In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.

Suggested Citation

  • Yong Wang & Qin Li & Xiangyang Guan & Jianxin Fan & Yong Liu & Haizhong Wang, 2020. "Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries," Sustainability, MDPI, vol. 12(15), pages 1-33, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:5966-:d:389035
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    References listed on IDEAS

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    Cited by:

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    2. Yong Wang & Jingxin Zhou & Yaoyao Sun & Xiuwen Wang & Jiayi Zhe & Haizhong Wang, 2022. "Electric Vehicle Charging Station Location-Routing Problem with Time Windows and Resource Sharing," Sustainability, MDPI, vol. 14(18), pages 1-31, September.
    3. Huang Huang & Xinwei Cuan & Zhuo Chen & Lina Zhang & Hao Chen, 2023. "A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm," Agriculture, MDPI, vol. 13(5), pages 1-18, May.
    4. Yong Wang & Jiayi Zhe & Xiuwen Wang & Yaoyao Sun & Haizhong Wang, 2022. "Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows," Sustainability, MDPI, vol. 14(11), pages 1-37, May.
    5. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.

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