Assessment of Sustainable Reverse Logistic Provider Using the Fuzzy TOPSIS and MSGP Framework in Food Industry

Abstract

As consumers become ever more conscious of environmental issues, socially responsible corporate practices, and government regulations, companies are increasingly motivated to incorporate reverse logistics (RLs) into their operations, thus raising the question of provider selection. In previous studies, the food industry generally lacked a systematic reference method for RLs provider selection, especially during the post-COVID-19 pandemic. This study aims to develop a comprehensive approach that combines a technique for order preference by similarity to ideal solution (TOPSIS) and multi-segment goal programming (MSGP) models to select optimal RLs providers. Furthermore, this method will enable decision makers (DMs) to evaluate and select the best RLs provider considering the limited resources of the business. This approach allows DMs to consider both qualitative and quantitative criteria, set multiple target segmentation expectations, and achieve optimal RLs provider selection. This study also provides case studies of applications by food manufacturers. The main finding is that considering multiple criteria in making a decision produces better results than using a single criterion.