Closed-form solution-based fuzzy utility vectors acquired from trapezoidal fuzzy pairwise comparison matrices using logarithmic quadratic programming for improving fuzzy AHP decision-making systems

Abstract

Closed-form solution-based fuzzy utility vectors acquired from trapezoidal fuzzy pairwise comparison matrices (TFPCMs) play significant roles in examining the quality of fuzzy evaluations in TFPCMs and improving the efficiency of fuzzy analytic hierarchy process based decision-making systems. This paper introduces a concept of consistent TFPCMs and develops crucial properties to expose inherent constraints among increasing spread indices, decreasing spread indices, and core and support uncertainty indices of fuzzy evaluations in a consistent TFPCM. The paper proposes two normalization frames called core normalized trapezoidal fuzzy vectors and support normalized trapezoidal fuzzy vectors. By categorizing all TFPCMs with the same order into two groups, two logarithmic quadratic programming models are respectively built to seek normalized trapezoidal fuzzy utility vectors from TFPCMs. The two logarithmic quadratic programming models are subsequently integrated into one whose closed-form solution is identified by Lagrange multiplier method. A closed-form solution-based consistency index is presented to figure out inconsistency degrees of TFPCMs. An illustration with one consistent TFPCM and five inconsistent TFPCMs is offered to reveal the use of the presented method and a study comparing the developed model with fuzzy mean methods and fuzzy eigenvector methods is conducted to demonstrate the performance and superiority of the closed-form solution-based fuzzy utility acquisition method.