Volume 10, Issue 4 (10-2020)                   2020, 10(4): 715-732 | Back to browse issues page

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Hatefi S M, Asadi H, Shams G. DESIGNING A FUZZY INFERENCE SYSTEM FOR CONTRACTOR SELECTION UNDER UNCERTAINTY. International Journal of Optimization in Civil Engineering. 2020; 10 (4) :715-732
URL: http://ijoce.iust.ac.ir/article-1-460-en.html
Abstract:   (1829 Views)
The increase in the number of construction projects and the involvement of a large amount of resources show that one of the most important actions of any construction project is to select the right contractor for the project. Delays in most construction projects and increased costs compared to initial estimates are often due to inadequacies by contractors, indicating that the contractor has not been properly selected. The complexities of the construction industry and the existing uncertainties have led experts to point out that choosing a contractor is a sensitive and difficult task. The purpose of this paper is to design a fuzzy inference system (FIS) to select the best contractor in conditions of uncertainty. The fuzzy inference system is a powerful tool for handling the uncertainties and subjectivities arising in the evaluation process of contractors. The proposed FIS has a two-step computational process in which 28 criteria are determined to evaluate the contractors. The proposed FIS is applied to evaluate and select the best contractor among 5 contractors considered by the general department of roads and urban development in Shahrekord. The studied criteria for evaluating contractors are categorized in six groups, including good history and credibility, equipment, management and specialized staff, economic-financial, skills-ability, and technical criteria. The results show that technical criteria are determined as the most important criteria for evaluating contractors. Furthermore, the results of applying the proposed FIS reveal that contractor C is the best contractor with the final score of 31.40.
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Type of Study: Research | Subject: Optimal design
Received: 2020/11/16 | Accepted: 2020/10/19 | Published: 2020/10/19

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