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.
Highlights
Owing to the diversification of customer requirements, the majority of comprehensive logistics companies usually provide both distribution and collection services
Both in theory and practice, reverse logistics networks are usually composed of several logistics facilities, and regarding the multitude of recycling nodes, the proposed Two-Echelon Reverse Logistics Network (TERLN) optimization problem can be considered as a variant of the multi-echelon multi-depot vehicle routing problem [4]
Step 1: Load the data file corresponding to the investigated network, and initialize parameters related to the algorithm such as: the population size (Ps), number of generations of the offspring population (NoG), number of objective functions (NoF), number of runs (NoR), crossover and mutation indexes (CI and MI)
Summary
Owing to the diversification of customer requirements, the majority of comprehensive logistics companies usually provide both distribution and collection services. Reverse logistics is undertaken to recycle used products when the company is concerned with imminent environmental problems, or to collect goods from customers for a new distribution cycle. Packages are thrown away by end-users after withdrawing their contents, and third-party recycling companies usually take the initiative to collect these containers in order to reduce waste, pollution or for economic purposes. Cooperation mechanism is an effective strategy to achieve logistics network optimization in general, and solve TERLN related problems in particular. Cooperation leads to the sustainable TERLN optimization, and the total savings of the collaborative logistics network will offer consistent profits to participating recycling facilities. We design a bi-objective mathematical model along with detailed notations and definitions, the proposed CW_NSGA-II routing optimization algorithm, and the MCRS method used to allocate generated profits.
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