A Cuckoo Search Algorithm Approach for Multi- Objective Optimization in Reverse Logistics Network under Uncertainty Condition

In this study, an efficient logistics network was designed to optimize both time and cost as the most effective factors using a mathematical model (two-objective fuzzy optimization) in a reverse logistics system. This paper attempted to determine the value of goods sent between return processing centers in any period of time in order to minimize the total cost and time of delay within supply chain. The fuzzy approach was adopted in order to consider uncertainty in reverse logistics network. The validity of model was measured through a model proposed by Azar Resin Chemical Industrial Company and then implemented and solved by GAMS software. According to the previous studies that implemented the model at a smaller scale, the problem revolved around designing NP-hard logistics network. Hence, exact methods cannot solve these problems on a large scale. Therefore, for solving the problem, Meta-Heuristic algorithms was used in this study. Because Cuckoo search algorithm has a high efficiency in comparison to other algorithms. In order to validate the newly proposed algorithm, the results were compared against the exact solution. The findings suggested that the proposed Cuckoo algorithm was sufficiently accurate to solve the problem and achieve values similar to exact solution.