Revisiting ranking accuracy within WASPAS method

Abstract

PurposeThe purpose of this paper is to analyze previous models of the concept of ranking accuracy within the weighted aggregated sum product assessment (WASPAS) method and make necessary refinements.Design/methodology/approachThis paper presents a correct combination of the weighted sum model (WSM) and weighted product model (WPM), which is usually performed on anad hocbasis in the literature.FindingsOne of the reasons of rarely conducting ranking accuracy analysis might be that some of the reported equations in the literature are confusing, and hence, accurate partial derivatives cannot be calculated. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed.Research limitations/implicationsA corrected WASPAS equation for optimal combination parameters is derived. Two examples are used to validate the formulations, and software implementation is provided. Because multiple attribute decision-making (MADM) has gained widespread attention from both the academia and industry, the findings of this paper help decision makers fully capitalize the concept of ranking accuracy and avoid possible confusions regarding the equations reported in the literature.Originality/valueWASPAS is a relatively new MADM method and has enjoyed a visible position in the MADM literature. In addition to its simplicity, the WASPAS method utilizes the concept of ranking accuracy by combining the well-known WSM and WPM. This combination realized via an optimization criterion brings unique opportunities for decision makers such as evaluating confidence intervals for relative significance of alternatives and reducing estimated variance of ranking results. Despite its crucial importance, the combination of WSM and WPM is usually performed on anad hocbasis in the literature. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed along with clarifying examples.