Abstract
Reverse logistics, which is induced by various forms of used products and materials, has received growing attention throughout this decade. In a highly competitive environment, the service level is an important criterion for reverse logistics network design. However, most previous studies about product returns only focused on the total cost of the reverse logistics and neglected the service level. To help a manufacturer of electronic products provide quality postsale repair service for their consumer, this paper proposes a multiobjective reverse logistics network optimisation model that considers the objectives of the cost, the total tardiness of the cycle time, and the coverage of customer zones. The Nondominated Sorting Genetic Algorithm II (NSGA‐II) is employed for solving this multiobjective optimisation model. To evaluate the performance of NSGA‐II, a genetic algorithm based on weighted sum approach and Multiobjective Simulated Annealing (MOSA) are also applied. The performance of these three heuristic algorithms is compared using numerical examples. The computational results show that NSGA‐II outperforms MOSA and the genetic algorithm based on weighted sum approach. Furthermore, the key parameters of the model are tested, and some conclusions are drawn.
Highlights
Reverse logistics is the process of planning, implementing, and controlling the flow of raw materials, in-process inventory, finished goods, and related information from the pointMathematical Problems in Engineering of consumption to the point of recovery or the point of proper disposal 1
This paper proposes a multiobjective optimisation model for a threeechelon reverse logistics network design problem, which determines the optimal location and the number of both the collection points and repair centres and the transportation flows between the customer zones and the facility sites
We presented a multiobjective integer nonlinear programming model for a three-echelon reverse logistic network design problem
Summary
Reverse logistics is the process of planning, implementing, and controlling the flow of raw materials, in-process inventory, finished goods, and related information from the point. The common performance measures include cost minimisation, customer satisfaction maximisation, cycle time minimisation, flexibility, and the overall efficiency of the reverse logistics system. The minimisation of the cost is commonly a major concern to be considered when building a reverse logistics network system, but the service level is a key factor when determining the survival and development of a company under the current economic environment, which is driven by customer values. To service providers, both the service level and the total service cost are major concerns 8.
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