The author identified the substantial knowledge gaps (KGs) by peer-reviewing important research documents in the field of applying trapezoidal fuzzy sets transfer functions to evaluate green supplier performance (GSP) and multi-criteria decision-making (MCDM) statistical methodologies. The identified KGs were validated using an extensive literature survey. As an objective, the author devised a sophisticated GS-MLH evaluation model, experienced advanced techniques based on TFs to evaluate the green supply chain performance of supplier industries.
The authors developed an MCDM robust integrated approach (RIA), combining the hybrid with MOOSRA using dominance theory to address the supplier evaluation problem. To quote the least fuzzy data from experts against multi-level hierarchical (GS-MLH) model and transform the fuzzy data into crisp values, the authors developed an MCDM mathematical equation, which evaluates the ARs for first-level architectures using fuzzy linguistic assessment for second-level GSC architectures.
This approach calculates the performance score of GS alternatives as a percentage. Ultimately, the research findings are supported by an empirical case study conducted in the automobile parts manufacturing business, providing evidence for the practical applicability of the research.
The author discovered a lack of advanced assessment models for monitoring the performance of GS using the GS-MLH model, as First KG, is accommodated. (2). As the second KG noted that previous research could not provide any proof of using MCDM mathematical, statistical equations to calculate crisp appropriateness ratings (ARs) for first-level designs using linguistic input provided by experts for second-level GSC architectures, this is fulfilled. (3) As the third KG, the authors determined that no robust statistical approach in MCDM can accurately measure the performance of Generalized Systems (GSs) in terms of percentage, except for crisp values based on dominance theory, which is addressed.
