Optimizing-Information-Granule-Based Consensus Reaching Model in Large-Scale Group Decision Making

In large-scale group decision making (LSGDM), the consensus result is expected to be realized explicitly through reconciling various preferences provided by decision makers based on their personalized viewpoints. An information-granule-consensus-based decision brings about high flexibility and promising aspects in group decision making. The consensus reaching proposals reported so far paid little attention to the merits of granular computing for managing LSGDM problems. This article concerns an extension of the well-known analytic hierarchy process to the LSGDM scenario using the optimizing-information-granule-based consensus reaching method. The consensus measurement is first quantified using coverage and specificity to derive the optimal cluster using the fuzzy C-means algorithm. Then, based on the optimization model of an information granule leading from numerical to interval representation, a novel construction model of information granule from interval representations to type-2 interval representation is developed, which yields the consistency of the obtained result instead of proceeding with an extra revision. To achieve the desired consensus, a preference modification algorithm is designed to detect the adjusted decision maker and further provide adjustment suggestions following the reference decision maker. Finally, a numeric study illustrates the effectiveness and flexibility of the proposed method.