PROJECT TITLE :

Multiobjective Optimization for Multiperiod Reverse Logistics Network Design

ABSTRACT:

Lately, the ever-rising come back streams for repair service have forced the electronics makers to expand their reverse logistics capacities. However, most existing papers on the reverse logistics network style neglected the time sensitivity of the come flows. Moreover, most of these investigations were primarily involved with the one objective issues of either minimizing the full price or maximizing the profit. In this paper, we tend to propose a biobjective mixed-integer linear programming model for the multiperiod design problem of a reverse logistics network for repair service. A multiperiod setting is taken into consideration to form the reverse logistics network flexible to accommodate the gradual changes in the capability of the facilities and also the network configuration. To resolve the NP-laborious downside with biobjective, we tend to develop a hybrid evolutionary algorithm that combines nondominated sorting genetic algorithm II (NSGA-II) with a local search technique. We have a tendency to compare the hybrid evolutionary algorithm with NSGA-II and ϵ-constraint methodology using numerical examples. The comparison results indicate that the hybrid evolutionary algorithm outperforms the NSGA-II in most cases. The ϵ-constraint methodology performs best for the little instances, however it cannot solve large instances inside reasonable time. Finally, an intensive parametric analysis is conducted and several managerial insights are derived.