Multi-objective optimization of reverse logistics network based on random weights and genetic algorithm

Abstract

Optimizing reverse logistics network is important to the sustainable development of logistics. In this paper, we fully considered environment effect and the waste recycling factors, such as locations, frequency, quality and quantity, and established a reverse logistics network multi-objective optimization model based on them. This model can ensure that both the whole cost of network and the negative impact on the environment are minimum. We improved the genetic algorithm (GA) by combining it with random weight method, and then apply it to solve the model. The simulation results show that solution is the global optimization, and the method we proposed is simpler and more effective than traditional algorithms.