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 value of goods sent between return products processing centers in any time period, in such a way to minimize 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 Co and then implemented and solved by GAMS software. According to previous studies and implementation of model at smaller scale, the problem revolved around designing NP-hard logistics network. Hence, exact methods cannot solve these problems on large scale, for which Cuckoo algorithm was considered. In order to validate the newly proposed algorithm, results were compared against the exact solution. The results suggested that the proposed Cuckoo algorithm was sufficiently accurate to solve the problem and achieve values similar to exact solution.