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Article type: Research Article
Affiliations: Infrastructure Department, Chongqing University of Arts and Sciences, Chongqing, China
Correspondence: [*] Corresponding author. Xu Chen, Infrastructure Department, Chongqing University of Arts and Sciences, Chongqing, China. E-mails: [email protected]; [email protected].
Abstract: With the rapid development of China’s construction industry, the competition in the construction industry is becoming increasingly fierce. Enterprises need to continuously improve their competitiveness in the market. Some non-core businesses can be outsourced to professional contractors. At present, contractors have more and more influence on the operation and development of enterprises. Whether it is the construction period or the quality of the project, it will have a greater impact on the operation of the construction project. In the environment of increasingly fierce market competition and increasing project quality requirements, for the construction project contracting enterprises, in order to achieve the goal of low cost and high quality, it is necessary to select the most suitable contractor on the basis of comprehensive consideration of multiple factors. The construction enterprise contractor selection is a classical multiple attribute group decision making (MAGDM) problem. In recent years, the MAGDM problem has become an important research field in modern decision science. This paper extends the EDAS method to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS method, 2-tuple linguistic Pythagorean fuzzy EDAS (2TLPF-EDAS) is built for MAGDM. Finally, a case study for construction enterprise contractor selection and some comparative analysis with the other methods show that the new method proposed in this paper is effective, reasonable and accurate.
Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs), EDAS method, construction enterprise contractor selection
DOI: 10.3233/JIFS-231063
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3233-3245, 2023
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