A Genetic Based Algorithm to a Dynamic Logistics Problem

Topics on reverse logistics have been discussed over the years. Most of them are dealt with by assuming that facilities are kept open once they are established, and no inventory is stocked at intermediate recycling stations. By simultaneously relaxing these two assumptions, this paper studies a reverse logistics system with multiple items of which each consists of some components among a variety of spare parts. The purpose is to maximize the total profit by establishing a production schedule and reverse logistics framework over a finite time periods for a logistics system. The mathematic model established in this research is a constrained non-linear integer programming problem. A genetic based algorithm with the help of linear programming is developed to find solutions to this problem. Computational experiments show that the proposed approach is superior to the well-known CPLEX software in terms of both solution quality and computational effectiveness.