ARASsort: A new sorting based multiple attribute decision-making algorithm

Multiple attribute decision-making (MADM) tools can effectively support the decision analysts in selecting the best alternative among many, ranking the alternatives in decreasing or increasing order of preference, or allocating the alternatives into pre-defined ordered classes/categories. Even though the literature provides the analyst with precious sorting-based MADM tools such as PROMSORT, UTADIS, AHPSort, TOPSISsort, and so forth, the majority of the methods can be found complex and hard to be understood by the researchers and practitioners who are not familiar with the mathematical notions and computations of MADM (distance calculation, threshold and preference function determination, and so on). To provide a simpler but powerful MADM tool aiming at sorting the alternatives into classes, this study proposes a sorting-based additive ratio assessment algorithm which is called ARASsort. For limiting (interval-based) and central (reference-based) profiles describing the categories, we have developed two algorithms: ARASsort-lp and ARASsort-cp, respectively. Their applicability was shown in two examples: green supplier evaluation and economic freedom evaluation of countries. The validity of algorithms was presented by demonstrating the class assignment similarities between the results obtained by ARASsort, VIKORsort, and TOPSISsort. The findings show that ARASsort works well because it shows a higher level of class assignment similarities with the other methods.