A rule-based approach for dynamic analytic hierarchy process decision-making

The analytic hierarchy process (AHP) is widely used in many multi-criteria decision-making problems and has been successfully applied to many practical cases. However, the AHP is time-consuming and the decision model is not agile enough for fast changing environment. To overcome this weakness, we develop a rule-based approach for dynamic AHP decision-making in changing environment. We analyse critical factors in the AHP decision process under uncertainty and propose to encode expert knowledge for change handling using event-condition-action rules. We propose a theorem and associated method to determine the change in ordering of decision alternatives based on event-condition-action rule-induced weight updates. We demonstrate the effectiveness of our approach using a case study of the supplier selection decision-making task of the steel and iron industry in Taiwan. The study shows that our mechanism can effectively reach the same level of decision quality as expert decision maker(s).