This study proposes a bipolar model for resolving multiple criteria decision-making (MCDM) problems, based on the integration of rough set theory and three-way decisions, for forming a hybrid bipolar model. It begins by dividing a decision space into three disjoint regions: negative, neutral, and positive states, in the next, a rough set theory based rule induction mechanism generates two sets of rules associated with the positive and the negative states respectively, termed as the positive and the negative rules. The two groups of rules are regarded the bipolar rough experience (knowledge) in the form of rules and granulized knowledge, and decision makers can then define a threshold to select the covered number of rules in the bipolar decision model. This novel approach not only supports decision makers to select or rank alternatives, but also identify rough knowledge for the addressed problem. As a result, this novel bipolar model transforms soft computing analytics into a comprehensible bipolar model for decision aids.
['Kao-Yi Shen', 'Gwo-Hshiung Tzeng']
A Novel Bipolar MCDM Model Using Rough Sets and Three-Way Decisions for Decision Aids