A hybrid Fuzzy C-Means Clustering-AHP framework to select construction contractors

Abstract

Selection of contractors is the process of hiring the most appropriate contractor to deliver the project within specific time, with good quality, and at low cost. Selection based on a set of qualification criteria has become essential to the success of construction projects and it outperforms the traditional lowest cost selection method. A common problem however is the compatibility of the selected contractor to the requirements of the project delivery method. This paper proposes a fuzzy logic-based contractor's selection framework that identifies, categorizes, and determines the relevance of contractor's selection factors, based on their degrees of belonging to a set of main selection criteria, using Fuzzy C-Means Clustering (FCM) technique; and determines their interrelated impact weight, using the Fuzzy Analytical Hierarchy Process (FAHP). The proposed framework reduces the dependence on subjective judgment in categorizing vague concepts; and addresses imprecision inherent in the process of determining contractor's selection criteria. It also outlines a future strategy that can help evaluating contractors, based on the selected project delivery method.