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Implementation of Cooperation for Recycling Vehicle Routing Optimization in Two-Echelon Reverse Logistics Networks

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  • Yong Wang

    (School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
    School of Management and Economics, University of Electronic Science and Technology, Chengdu 610054, China)

  • Shouguo Peng

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

  • Kevin Assogba

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

  • 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)

  • Maozeng Xu

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

  • Yinhai Wang

    (Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA
    Transportation Data Science Research Center, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

The formation of a cooperative alliance is an effective means of approaching the vehicle routing optimization in two-echelon reverse logistics networks. Cooperative mechanisms can contribute to avoiding the inefficient assignment of resources for the recycling logistics operations and reducing long distance transportation. With regard to the relatively low performance of waste collection, this paper proposes a three-phase methodology to properly address the corresponding vehicle routing problem on two echelons. First, a bi-objective programming model is established to minimize the total cost and the number of vehicles considering semitrailers and vehicles sharing. Furthermore, the Clarke–Wright (CW) savings method and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are combined to design a hybrid routing optimization heuristic, which is denoted CW_NSGA-II. Routes on the first and second echelons are obtained on the basis of sub-optimal solutions provided by CW algorithm. Compared to other intelligent algorithms, CW_NSGA-II reduces the complexity of the multi-objective solutions search and mostly converges to optimality. The profit generated by cooperation among retail stores and the recycling hub in the reverse logistics network is fairly and reasonably distributed to the participants by applying the Minimum Costs-Remaining Savings (MCRS) method. Finally, an empirical study in Chengdu City, China, reveals the superiority of CW_NSGA over the multi-objective particle swarm optimization and the multi objective genetic algorithms in terms of solutions quality and convergence. Meanwhile, the comparison of MCRS method with the Shapley value model, equal profit method and cost gap allocation proves that MCRS method is more conducive to the stability of the cooperative alliance. In general, the implementation of cooperation in the optimization of the reverse logistics network effectively leads to the sustainable development of urban and sub-urban areas. Through the reasonable reorganization of the entire network, recycling companies can provide more reliable services, contribute to the reduction of environmental pollution, and guarantee significant profits. Thus, this paper provides manufacturing companies, logistics operators and local governments with tools to protect the environment, while still making profits.

Suggested Citation

  • Yong Wang & Shouguo Peng & Kevin Assogba & Yong Liu & Haizhong Wang & Maozeng Xu & Yinhai Wang, 2018. "Implementation of Cooperation for Recycling Vehicle Routing Optimization in Two-Echelon Reverse Logistics Networks," Sustainability, MDPI, vol. 10(5), pages 1-27, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1358-:d:143521
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    2. Anderluh, Alexandra & Nolz, Pamela C. & Hemmelmayr, Vera C. & Crainic, Teodor Gabriel, 2021. "Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in urban logistics," European Journal of Operational Research, Elsevier, vol. 289(3), pages 940-958.
    3. 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.
    4. Seung Yoon Ko & Ratna Permata Sari & Muzaffar Makhmudov & Chang Seong Ko, 2020. "Collaboration Model for Service Clustering in Last-Mile Delivery," Sustainability, MDPI, vol. 12(14), pages 1-18, July.
    5. Sonu Rajak & K. E. K. Vimal & Sricharan Arumugam & Jagadesan Parthiban & Swesh Kannan Sivaraman & Jayakrishna Kandasamy & Angel Acevedo Duque, 2022. "Multi-objective mixed-integer linear optimization model for sustainable closed-loop supply chain network: a case study on remanufacturing steering column," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6481-6507, May.
    6. Yong Wang & Yingying Yuan & Xiangyang Guan & Haizhong Wang & Yong Liu & Maozeng Xu, 2019. "Collaborative Mechanism for Pickup and Delivery Problems with Heterogeneous Vehicles under Time Windows," Sustainability, MDPI, vol. 11(12), pages 1-30, June.

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