Intelligent Collaborative Support System for AHP-Group Decision Making

Abstract

Analytic hierarchy process group decision making obtains the group's collective preference relationship based on the pair-wise comparative matrix of each decision maker by means of some aggregation rules, such as the aggregation of individual judgments and the aggregation of individual priorities. Then, consensus-reaching models are employed to help decision makers improve the consensus degree when the results are undesirable. A new preference aggregation method is proposed to integrate individual preference, which occurs when the group preference is nearest to all the individual priority vectors with weights by means of different optimization models. We further developed intelligent consensus-reaching model by adjusting the individual pair-wise comparison matrix in order to improve the acceptable individual priorities compared to the proposed group preference by the optimization models. The methods can directly improve a consensus-reaching degree without interactive preference modifications base on simple group preference aggregation method with row geometric mean. Some illustrative examples are examined to demonstrate the proposed models for application.