cuckoo optimization algorithm

Over product oriented course to industry one, firms' competitiveness is being complicated to gain more portion of market that lead to dynamic and more variation environment. In this situation, customers find more authority to select their favorite products and services. Response to market fluctuation to supply customers' needs is considered as an important tool to firms' promotion. Needs to reduce costs and improve organization process cause to pay more attention to supply chain concept. The main goal of each supply chain satisfies customers' needs with the lowest cost and highest efficiency.Structurally, supply chain includes retailor, wholesaler, distributor, manufacturer, and supplier. An efficient logistic network should be designed so that response to uncertainty. Since time and cost are the most important factors in reverse logistic design, a fuzzy two objectives optimization model is proposed in this study. First, a fuzzy mathematical programming model presented. The aim of this model is determining delivery goods amount among centers so that total cost and total delay time are minimized. In this research is used fuzzy approach to cover uncertainty in reverse logistic network. A numerical example has been produced and solved by GAMS. In order to solve the problem in large scale a cuckoo optimization meta heuristic algorithm is developed. The results indicate that the total amount sent to the manufacturer of the values obtained from the exact solution and the objective function value decreases with increasing number of iterations which this indicates proper / valid operation is the proposed algorithm.