An ordinal-cardinal consensus adjustment allocation mechanism for large-scale group decision making based on the consistency of distributed preference relations under fuzzy uncertainty

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2024-08-23 DOI:10.1016/j.cie.2024.110504
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Abstract

Large-scale group decision making (LSGDM) problem is common in real life. With the increase in the number of alternatives and the limited rationality of human beings, consistency problem is inevitable when pairwise comparison method is used. We propose an improved consistency calculation approach to generate consistent distributed preference relations (DPRs), which adopts adjacent score intervals to calculate the score intervals of non-adjacent alternative pairs. By using optimization model, the initial DPR is preserved as much as possible on the premise of order consistency. As for the consensus analysis, the concept of relationship-possibility degree is defined to capture the ignorance and fuzzy uncertainty in assessments. An ordinal consensus measure method considering absolute position difference and relative position dissimilarity with relationship-possibility degree is proposed. Ordinal-cardinal consensus adjustment model based on DPR is then constructed to obtain the minimum consensus adjustment of decision makers or subgroups coalition which are considered as coalition payoff. In addition, to distribute the ordinal-cardinal minimum consensus adjustment reasonably, we construct a two-stage consensus adjustment allocation mechanism adopting the improved multi-weighted Shapley function in the cooperative game. Several optimization models are constructed to obtain the adjusted DPRs of decision makers or subgroups. Finally, an illustrative example is presented to demonstrate the validity of the proposed method in dealing with the decision problems of product development engineering. It is expected to make the LSGDM procedure in a more intelligent way.

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基于模糊不确定条件下分布式偏好关系一致性的大规模群体决策的序-心一致调整分配机制
大规模群体决策(LSGDM)问题在现实生活中很常见。随着备选方案数量的增加和人类理性的有限性,使用成对比较法时不可避免地会出现一致性问题。我们提出了一种改进的一致性计算方法来生成一致的分布式偏好关系(DPRs),它采用相邻得分区间来计算非相邻备选方案对的得分区间。通过使用优化模型,在顺序一致性的前提下尽可能保留初始 DPR。在共识分析方面,定义了关系可能性度的概念,以捕捉评估中的无知和模糊不确定性。提出了一种考虑绝对位置差异和相对位置差异以及关系可能性度的顺序共识度量方法。然后,构建基于 DPR 的序数-心形共识调整模型,以获得决策者或分组联盟的最小共识调整值,并将其视为联盟报酬。此外,为了合理分配顺序-心数最小共识调整,我们在合作博弈中采用改进的多权重沙普利函数构建了两阶段共识调整分配机制。我们还构建了多个优化模型,以获得决策者或分组的调整后 DPR。最后,举例说明了所提方法在处理产品开发工程决策问题时的有效性。它有望使 LSGDM 程序更加智能化。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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