Performance analysis of fuzzy analytic hierarchy process multi-criteria decision support models for contractor selection

Multi-Criteria Decision Making (MCDM) is a discipline aimed at supporting decision-makers to settle on an ideal choice within the sight of different and clashing criteria. MCDM strategies have developed to oblige different kinds of utilizations. One comprehensively utilized MCDM strategy is the Analytic Hierarchy Process (AHP). AHP, in spite of its simplicity in idea, it can't consider imprecise input. Throughout the years, fuzzy logic has demonstrated its effectiveness as a MCDM technique. It includes a few preferences inside uncertain, imprecise and obscure settings than AHP and other MCDM strategies. Consolidating Fuzzy strategies with AHP is one methodology for taking care of the entangled issues of AHP. The significance of this study is to provide baseline information to the construction clients and consultants on the importance of contractor's prequalification decision criteria to be adopted, which will eventually translate to a better decision making and increase project performance. Taking into account our past research, this paper proposed a Feedback Integrated Fuzzy Analytic Hierarchy Process (FAHP) model for ranking decision criteria for contractual worker determination by combining the selection process and consistency control module. This consistency control module contains a feedback module that generates advice to decision makers so as to check the irregularity in their decisions' during pairwise comparisons. In this paper, the performance investigation of the proposed FAHP model is validated on datasets from three diverse FAHP models. The proposed model accuracy obtained when tested with three distinctive datasets were 98.2%, 99.99%, and 98.24% respectively. The results established the adequacy and uselfulness of the proposed FAHP model in ranking contractor decision criteria. The research additionally guide decision-makers on picking distinctive FAHP algorithms in assessing and ranking decision criteria utilized in contractors selection.