Robust Fuzzy Decision Support Framework for Comprehensive Evaluating of Food Supply Chain Performance

Improving efficiency, reducing expenses, and ensuring sustainability in today’s competitive market is made possible by optimising the performance of the food supply chain (FSC). A strong decision-making framework is required to overcome obstacles including high transportation and production costs, uneven quality, and the impact on the environment. To solve these problems, this paper presents an all-inclusive multi-criteria decision making (MCDM) framework that uses enhanced relative utility and nonlinear standardisation (ERUNS) to rank alternatives, Logarithmic percentage change-driven objective weighing (LOPCOW) to determine objective weights, and subjective weight assignment by ratio analysis (SWARA) to determine subjective criterion weights. LOPCOW offers objective weights grounded in facts and indifference thresholds, whereas SWARA records decision-makers (DMs) preferences in weighing factors like sustainability and cost efficiency. The ERUNS methodology then uses utility degree assessments and matrix standardization to rank and evaluate alternatives. An improved method of assessment is to make use of intuitionistic fuzzy sets (IFS), which deal with ambiguity by adding membership, non-membership, and hesitation levels. By merging subjective and objective data, this integrated method maximizes FSC performance, provides useful insights for enhancing overall efficiency and sustainability, and offers a viable answer to contemporary difficulties. It also offers helpful insights for improving overall efficiency and sustainability, and it provides a practical response to the challenges that are now being faced.