A Soft Computing Approach for group decision making: A supply chain management application

This paper presents a novel Soft Computing Approach called Neuro-Fuzzy Analytical Network Process (NFANP)'' for the group decision-making problems based on the conventional Analytic Network Process (ANP) method. The proposed approach deals with the interval values of judgments in a fuzzy environment using mobile, not fixed, trapezoidal and triangular membership functions, as well as the interval numerical ratio defined by alpha-cuts and the decision maker's confidence levels. The consistency problem of the fuzzy reciprocal matrices is addressed in the proposed paper by allowing a certain tolerance deviation to be less than 0.20. Furthermore, trained Artificial Neural Networks (ANNs) are included in the proposed approach to reduce the large number of computations of the arithmetic operations required to correlate decision factors with the alternatives. In the proposed implementation, the selection problem is defined into three main decision groups: Supplier Characteristics, On-Going Performance, and Project Management Capabilities. The supplier alternatives are classified by the decision makers corresponding to company size, quality system implementation, and cost management. The application of the proposed approach shows a great accuracy in the final utility values and a significant reduction in the calculation requirements. (C) 2020 Elsevier B.V. All rights reserved.