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

Yong Wang, Shouguo Peng, Kevin Assogba, Yong Liu, Haizhong Wang, Maozeng Xu and Yinhai Wang
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Yong Wang: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, 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

Sustainability, 2018, vol. 10, issue 5, 1-27

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.

Keywords: n/a (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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