A Supplier Selection Model Using the Triangular Fuzzy-Grey Numbers - Shervin Zakeri; Dimitri Konstantas; Reidar Brumer Bratvold; Dragan Pamucar (IEEE Access, 2023)
Selecting suppliers involves making decisions based on multiple criteria and options, making it a multi-criteria decision-making (MCDM) problem. Optimal decisions can help the entire supply chain to reduce costs and increase efficiency. However, uncertainty in supplier selection can increase the risk of incorrect choices and unforeseen consequences, which may stem from criteria weights or supplier performance. To address these challenges, this paper presents the Triangular Fuzzy-Grey (TFG) system, an innovative approach for MCDM problems. Integrating grey numbers and triangular fuzzy numbers, the TFG system extends fuzzy logic. Grey systems and fuzzy numbers are valuable tools in MCDM, each with their own advantages and limitations. Grey systems exhibit robustness in handling uncertain and incomplete information, aided by their intuitive models. They offer prediction capabilities and adaptability, although they may provide approximate solutions and rely on expert judgment, lacking a comprehensive theoretical foundation. Fuzzy numbers excel in handling uncertainty, accommodating vague data, and expressing linguistic preferences. They facilitate criteria aggregation and accommodate various decision variables. Combining grey systems and fuzzy numbers enhances decision-making, leveraging their strengths to address uncertainty and improve accuracy. The TFG system effectively handles uncertainty by assigning higher probabilities to smaller, more certain areas. To demonstrate its effectiveness, an integrated TFG WLD-SAW model is proposed for green supplier selection, where Supplier No.2 with hat $\mathbb {SS} _{i}=3.3202$ is selected as the best green supplier. Comparative analysis using the Zakeri-Konstantas weighted rankings similarity measure shows that TFG WLD achieves similar results to fuzzy and grey WLD methods in solving the green supplier selection problem.