Decision Rules-Based Probabilistic MCDM Evaluation Method – An Empirical Case from Semiconductor Industry

Abstract

Dominance-based rough set approach has been widely applied in multiple criteria classification problems, and its major advantage is the inducted decision rules that can consider multiple attributes in different contexts. However, if decision makers need to make ranking/selection among the alternatives that belong to the same decision class—a typical multiple criteria decision making problem, the obtained decision rules are not enough to resolve the ranking problem. Using a group of semiconductor companies in Taiwan, this study proposes a decision rules-based probabilistic evaluation method, transforms the strong decision rules into a probabilistic weighted model—to explore the performance gaps of each alternative on each criterion—to make improvement and selection. Five example companies were tested and illustrated by the transformed evaluation model, and the result indicates the effectiveness of the proposed method. The proposed evaluation method may act as a bridge to transform decision rules (from data-mining approach) into a decision model for practical applications.