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Group-Oriented Multi-Attribute Decision-Making Method Based on Dominance Rough Set Theory

Group-Oriented Multi-Attribute Decision-Making Method Based on Dominance Rough Set Theory

Yu Bin, Xiao Zeyu, Dai Yinglong
Copyright: © 2024 |Pages: 34
ISBN13: 9798369315828|ISBN13 Softcover: 9798369363911|EISBN13: 9798369315835
DOI: 10.4018/979-8-3693-1582-8.ch001
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MLA

Bin, Yu, et al. "Group-Oriented Multi-Attribute Decision-Making Method Based on Dominance Rough Set Theory." Big Data Quantification for Complex Decision-Making, edited by Chao Zhang and Wentao Li, IGI Global, 2024, pp. 1-34. https://doi.org/10.4018/979-8-3693-1582-8.ch001

APA

Bin, Y., Zeyu, X., & Yinglong, D. (2024). Group-Oriented Multi-Attribute Decision-Making Method Based on Dominance Rough Set Theory. In C. Zhang & W. Li (Eds.), Big Data Quantification for Complex Decision-Making (pp. 1-34). IGI Global. https://doi.org/10.4018/979-8-3693-1582-8.ch001

Chicago

Bin, Yu, Xiao Zeyu, and Dai Yinglong. "Group-Oriented Multi-Attribute Decision-Making Method Based on Dominance Rough Set Theory." In Big Data Quantification for Complex Decision-Making, edited by Chao Zhang and Wentao Li, 1-34. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1582-8.ch001

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Abstract

This chapter proposes group-oriented multi-attribute decision-making based on mixed advantage-disadvantage degree (GOMADMMADD) method to deal with group-oriented decision analysis. Based on the strict attribute relationship between group members, this method proposes the concepts of “local advantage-disadvantage degrees” and “advantage-disadvantage degrees,” which solves the challenge of group-oriented multi-attribute decision-making (GOMADM). However, this method still has the problem that the number of groups to be evaluated increases exponentially. Therefore, the authors first improved the GOMADMMADD method and proposed decision-making method based on dominance-based rough sets (GMADMDRS). Then, the “advantage” neighborhood operators and “disadvantage” neighborhood operators of groups are introduced to define the “advantage-disadvantage neighborhood degree” (ADND), and the GMADMDRS method is optimized by using ADND. The experimental results show that both GOMADMMADD method and GMADMDRS method effectively evaluate the population, and the optimized GMADMDRS algorithm is consistent in the experimental results, and the time performance has been greatly improved, and the computational performance has been significantly improved, which has practical significance to solve the problem of exponential growth of the number of groups to be evaluated. These methods provide a novel perspective and effective method for group oriented multi-attribute decision-making.

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