Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization

This paper presents a fuzzy optimization model to solve multicriteria decision making (MCDM) systems based on a fuzzy analytic hierarchy process (fuzzy AHP). To deal with the imprecise judgments of decision makers, a fuzzy AHP decision making model is proposed as an evaluation tool, where the expert’s comparison judgments are translated into fuzzy numbers. Unlike the conventional fuzzy AHP methods, the proposed method drives exact weights from consistent and inconsistent fuzzy comparison matrices, which eliminate the need of additional aggregation and ranking procedures. The proposed method transforms a fuzzy prioritization problem into a constrained nonlinear optimization model. An improved particle swarm optimization (PSO) is applied to solve the optimization model as a nonlinear system of equations. Several illustrative examples using existing fuzzy AHP methods are given to demonstrate the effectiveness of the proposed method.