A reverse logistics inventory model with multiple production and remanufacturing batches under fuzzy environment

In the last few years, inventory modeling with reverse logistics has received more attention from both the academic world and industries. Most of the existing works in the literature believed that newly produced products and remanufactured products have the same quality. However, in many industries, customers do not consider remanufactured products as good as new ones. Therefore, this study develops a reverse logistics inventory model with multiple production and remanufacturing batches (cycles) under the fuzzy environment where the remanufactured products are of subordinate quality as compared to the newly produced products. As the precise estimation of inventory cost parameters such as holding cost, setup cost, etc. becomes often difficult; so these cost parameters are represented as triangular fuzzy numbers. Used products are purchased, screened and then suitable products are remanufactured. The production and remanufacturing rates are demand dependent. The main goal of this study is to obtain the optimal production and remanufacturing policy that minimizes the total cost per unit time of the proposed inventory system. The signed distance method is employed to defuzzify the total cost function. A numerical example is presented to demonstrate the developed model. Finally, sensitivity analysis is executed to study the impact of key parameters on the optimal solution