A spherical Z-number multi-attribute group decision making model based on the prospect theory and GLDS method

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Meiqin Wu, Sining Ma, Jianping Fan
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Abstract

Multi-attribute group decision-making is an important research field in decision science, and its theories and methods have been widely applied to engineering, economics and management. However, as the information embedded volume and complexity of decision-making expand, the diversity and heterogeneity of decision-making groups present significant challenges to the decision-making process. In order to effectively address these challenges, this paper defines the concept of spherical Z-number, which is a fuzzy number that takes into account a wide range of evaluation and its reliability. Additionally, a group decision-making model in a spherical Z-number environment is proposed. First, an objective phased tracking method is used to determine expert weights, maintain the consistency in decision-making group evaluations. The gained and lost dominance score method is combined with prospect theory to integrate expert psychological behavior when facing risks. The proposed method considers both group utility and individual regret, and balances the gains and losses of various options in the decision-making process. Finally, in response to the 3R principle, the model is employed to address the shared e-bike recycling supplier selection problem and to assess the viability of the decision-making outcomes. The results demonstrate that the model is robust in the context of varying parameter configurations. Moreover, the correlation coefficients between its ranking outcomes and those of alternative methodologies are all above 0.77, and its average superiority degree is 1.121, which is considerably higher than that of other methods. Consequently, the model's effectiveness and superiority are substantiated.

Abstract Image

基于前景理论和 GLDS 方法的球形 Z 数多属性群体决策模型
多属性群体决策是决策科学的一个重要研究领域,其理论和方法已被广泛应用于工程、经济和管理领域。然而,随着决策所蕴含的信息量和复杂性的不断扩大,决策群体的多样性和异质性给决策过程带来了巨大的挑战。为了有效应对这些挑战,本文定义了球形 Z 数的概念,它是一种考虑到广泛评价及其可靠性的模糊数。此外,本文还提出了球形 Z 数环境下的群体决策模型。首先,采用客观分阶段跟踪法确定专家权重,保持决策小组评价的一致性。得失优势得分法与前景理论相结合,整合了专家面对风险时的心理行为。所提出的方法既考虑了群体效用,又考虑了个体遗憾,平衡了决策过程中各种方案的得失。最后,根据 3R 原则,该模型被用于解决共享电动自行车回收供应商选择问题,并评估决策结果的可行性。结果表明,该模型在不同参数配置的情况下是稳健的。此外,其排序结果与其他方法之间的相关系数均在 0.77 以上,平均优越度为 1.121,大大高于其他方法。因此,该模型的有效性和优越性得到了证实。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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