Reverse logistics and lean, agile, resilient, and green paradigms in supply chain: A comparative analysis of fuzzy decision-making methods

• Proposing a fuzzy decision framework to enhance Supply Chain (SC) performance. • Using Reverse logistic (RL) and Lean, Agile, Resilient, and Green (LARG) in SC. • Presenting hybrid SWARA-WASPAS methods to prioritize RLs solutions in LARG SC. • Assenting sensitivity analysis and validation with different decision methods. • Improving productivity by a new application of RL and LARG paradigms in paper SC. Reverse logistic (RL) systems in the Supply Chain (SC) are not efficient due to challenges in inventory management models and ordering policies. This research combines paradigms of Lean, Agile, Resilient, and Green (LARG) and provides solutions for executing RLs. The main objective of the research is to specify and prioritize the solutions for implementing RLs based on LARG paradigms. In this regard, the Fuzzy Step-wise Weight Assessment Ratio Analysis (F-SWARA) and the Fuzzy Weighted Aggregates Sum-Product Assessment (F-WASPAS) approaches are proposed to prioritize solutions. The F-SWARA method weighs the indicators, and the F-WASPAS technique is used to prioritize the solutions. To ensure robustness, various methods are employed for validation, and sensitivity analysis is examined. The findings showed that the sub-indicators of cost reduction, reducing business waste, and improving cash-to-cash cycle with importance levels of 0.228, 0.174, and 0.163 were the most significant factors, respectively. Also, solutions of investment in the development of return management software and the use of artificial intelligence to predict product returns, the development of outsourcing strategies taking into account cost, environmental regulations, and SC structure, and using technologies of information and communication to improve coordination and communication between SC players were introduced as superior solutions, respectively.