4.7 Article

Pythagorean 2-Tuple Linguistic VIKOR Method for Evaluating Human Factors in Construction Project Management

期刊

MATHEMATICS
卷 7, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/math7121149

关键词

multiple attribute group decision making (MAGDM); Pythagorean 2-tuple linguistic numbers (P2TLNs); P2TLWA operator; P2TLWG operator; VIKOR method

资金

  1. National Natural Science Foundation of China [71571128]
  2. Humanities and Social Sciences Foundation of Ministry of Education of the People's Republic of China [14YJCZH091]

向作者/读者索取更多资源

Since the reform and opening up, Chinese economic and social development has undergone great changes, and the people's living standards have improved markedly. For the national economy, the engineering construction is not only a carrier for specific economic tasks, but also a driving force for rapid and sustained economic development. With the continuous expansion of the scale of construction projects, safety management problems of construction projects are constantly exposed. How to effectively avoid accidents has become an important issue to be solved urgently in the construction industry. This paper mainly evaluates human factors in the process of construction project management, such as workers' proficiency, workers' safety awareness, technical workers' quality, and workers' emergency capacity, with the purpose of helping China's construction projects proceed smoothly. In this research, we provide a multiple attribute group decision-making (MAGDM) technique based on Pythagorean 2-tuple linguistic numbers (P2TLNs) and the VIseKriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) method for evaluating the human factors of construction projects. P2TLNs are used to represent the performance assessments of decision makers. Relying on a P2TLWA operator, P2TLWG operator, and the essential VIKOR method, a general framework is established. An application is presented to test the validity of the new method, and a comparative analysis with two algorithms and the P2TL-TODIM method is illustrated with detail.

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