Fuzzy reverse logistics inventory model of smart items with two warehouses of a retailer considering carbon emissions

Abstract

Running the business smoothly for protecting the environment is a significant challenge, on which industries are trying something to do at their level best. Reverse logistics play an important role in system design by reducing environmental consequences and increasing economic and social impacts. Given the recent fluctuations of the market, the production cost and ordering cost are considered triangular fuzzy numbers in this study. Customers’ demand is met at the right time, and there is no shortage of items; thus, attention can be paid to two warehouses of a retailer. The setup costs Purchasing costs and deterioration costs of this system are affected by the learning effects, which lead to a decrease in the total cost. Inflation is a significant problem in the market because manufacturing, remanufacturing, and retailers are all affected. This study proposes a reverse logistics system model so that customers can resolve their complaints about defective items and carbon emissions under two warehouses. Numerical results show that the fuzzy model is more economically beneficial than the crisp model, finds that the crisp and fuzzy model saw a difference of 0.34% in total cost. Two numerical examples illustrate this study, and a sensitivity analysis is performed using tables and graph.