A GA Model Development for Decision Making Under Reverse Logistics

Managing products’ end-of-life and recovery of used products is gaining significant importance during last years. Therefore, managing the reverse flow of products can be an important potential for winning consumers in future competitive markets. In this context, establishing reverse logistics networks is becoming a main problem in reverse supply chains. Genetic Algorithm (GA) is utilized to solve the proposed NP-hard problem and find the best possible design for different facilities. In order to test the applicability of proposed GA, we suppose a tire reverse logistic case and solve the problem. The results show that the least cost will be achieved by using the free space of distribution centers and integrating collection and inspection centers within them. In addition, we suggest using hybrid algorithm in future allocation problems to obtain best solutions.