In Qualifications-Based Selection (QBS), consultants are selected according to their competencies rather than price. However, clients are often apprehensive about the subjectivity associated with implementing QBS because non-price criteria are hard to measure. In addition, there is no complete set of all relevant consultant evaluation criteria established. There is also a lack of an automated decision support system for objectively assisting owners in selecting qualified consultants with improved consistency and transparency. In this paper, a comprehensive set of consultant evaluation criteria is identified. Evaluation rules are also established for measuring qualitative criteria, where those rules determine the linguistic performance ratings for the fuzzy TOPSIS model instead of decision-makers, which minimizes subjectivity and increases transparency. The decision support system presented in this paper is flexible, allowing the decision-maker to adjust criteria weights based on the project characteristics and to exclude any non-applicable evaluation rules that may not fit in some projects.

Among the critical factors of project success is the proper selection of qualified contractors. More owners are adopting Best Value (BV) procurement which combines the technical qualifications with fee proposals to rank bidders. However, the successful application of BV depends on two integral factors: ranking of contractor’s qualifications using multiple criteria and the weights assigned to each selection criterion. Many criteria are qualitative, leading to difficulties in objectively quantifying a contractor’s rank. Thus, this paper provides owners with a simplified decision-support methodology that quantifies criteria that owners might consider in their decisions, reduces the number of comparisons that owners must perform, and reduces bias in defining criteria weights. This was achieved by first collecting the criteria commonly used to assess contractors’ competence. Then, criteria were analyzed and clustered into separate, quantifiable groups. Third, pairwise comparison was employed to devise a criteria-weighting method based on the clustered groups and determine the scores of bidding contractors. The proposed methodology could help identify the areas of strengths and weaknesses of each contractor in comparison with other bidders. The method presents a scientific and practical approach and, thus, can be considered for future applications.

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