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Article type: Research Article
Authors: Zhang, Xianga | Huang, Jianhuaa; * | Fang, Litinga | Li, Qianb
Affiliations: [a] School of Economics and Management, FuzhouUniversity, Fuzhou, China | [b] School of Management, Xi’an University of Architecture and Technology, Xi’an, China
Correspondence: [*] Corresponding author. Jianhua Huang, School of Economics and Management, Fuzhou University, No. 2 Longjing Avenue, Fuzhou, China. Email: everam@fzu.edu.cn.
Abstract: Selecting suppliers for prefabricated components (PCs) involves a complex decision-making process, frequently relying on ambiguous information and subjective judgment. However, most existing methods use precise values to portray indicator information and overlook the uncertainty of weights and the subjective preferences of decision-makers (DMs). In order to address these limits, this paper proposes a novel approach to select suppliers of PCs. Initially, an evaluation index system for suppliers is established through literature analysis and a questionnaire survey. The system comprises six layers: product quality, price, service level, comprehensive ability, supply ability, and environmental sustainability. The group decision matrix is then constructed using the set-valued statistical method and the prospect theory. The index weights are determined by a combination weighting method. Next, the cobweb model is introduced to analyze the disparity between the alternative and ideal solutions, describing their similarities in terms of area and shape. Lastly, cobweb similarity is employed instead of comprehensive distance, combined with the minimum sum of squares criterion, to improve the closeness algorithm and contrast the alternatives. The results demonstrate that this method facilitates a comprehensive evaluation of the benefits and drawbacks of various alternatives from diverse perspectives. Furthermore, it allows flexible adjustments based on the risk preferences of DMs, ensuring accurate and reliable decision results.
Keywords: Select suppliers, risk preference, prospect theory, cobweb model, cobweb similarity
DOI: 10.3233/JIFS-232027
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4467-4479, 2024
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