Abstract:
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.
Machine summary:
com A Cuckoo Search Algorithm Approach for Multi- Objective Optimization in Reverse Logistics Network under Uncertainty Condition Reza Ehtesham Rasi*, a a Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Abstract 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.
Designing and implementing reverse logistics network for product returns will not only curtail inventory and transportation costs, but will also increase customer loyalty (Lee et al.
The reverse logistics network design covers a wide range of applications from linear models to complex non-linear models, minimizing cost of shipping in complex multi-objective optimization problems (Altiparmak et al.
Min (1989) proposed a goal programming model to select shipping method that minimizes costs of transportation and reverse distribution against time of product shipping under recall circumstances.
(View the image of this page) (8) The first objective function of new model is the minimization of the reverse logistics costs including fixed cost of reopening the processing centers, shipping cost at return centers, processing centers, manufacturer, and inventory cost of the processing centers.
In this study, the time-cost was optimized in the multi-objective fuzzy model through Cuckoo search algorithm.