A cleaner supplier selection model using rate-weight connected vectors processor (RWCVP): Type I

Abstract

Addressing uncertainty is crucial when it comes to solving the problem of selecting cleaner suppliers. Uncertainty in this context stems from various sources, such as the lack of information regarding the specific details of the criteria and the absence of information about suppliers' performance in meeting the green/sustainable criteria. Multi-criteria decision-making (MCDM) methods are powerful tools for resolving cleaner supplier selection (CSS) problems. One common limitation faced by MCDM methods is their difficulty in handling uncertainty. This requires the inclusion of additional tools such as fuzzy logic, rough sets, and similar techniques. Nevertheless, their utilization introduces a certain level of risk due to fundamental disparities encompassing distinct scales, functions, operations, and philosophies. This paper outlines various scenarios involving a recently developed MCDM method known as rate-weight connected vectors processor (RWCVP), specifically Type I, to tackle these challenges within the context of CSS. By utilizing the computation of three distinct triangle areas established between two vectors of rate and weight, the original RWCVP is extended to four scenarios including 1. Solving fuzzy CSS; 2. Solving CSS problems with uncertain weights; 3. Solving CSS problems with uncertain rates; 4. Solving CSS with uncertain weights and rates. The new methods are applied to three real-world examples of CSS problems in which Supplier 1 with (Si˜) = 2.656, Supplier 4 with (Si˜) = 17.518, and Supplier 1 with (Si˜) = 1.007 are selected as the best suppliers for the first, second, and third examples, respectively. Employing Zakeri-Bratvold rank correlation; Zakeri-Bratvold what-if analysis, the obtained rankings underwent a comprehensive examination. The findings demonstrated that RWCVP method was insusceptible to rank reversals, exhibited distinct behavior in generating rankings compared to other MCDM methods, and provided reliable results.