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Direct Iterative Procedures for Consensus Building with Additive Preference Relations Based on the Discrete Assessment Scale

机译:基于离散评估量表的具有加性偏好关系的共识构建的直接迭代过程

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摘要

Individual consistency and group consensus are both important when seeking reliable and satisfying solutions for group decision making (GDM) problems using additive preference relations (APRs). In this paper, two new algorithms are proposed to facilitate the consensus reaching process, the first of which is used to improve the individual consistency level, and the second of which is designed to assist the group to achieve a predefined consensus level. Unlike previous GDM studies for consistency and consensus building, the proposed algorithms are essentially heuristic, modify only some of the elements in APRs to reduce the number of preference modifications in the consistency and consensus process, and have modified preferences that belong to the original evaluation scale to make the generated suggestions easier to understand. In particular, the consensus algorithm ensures that the individual consistency level is still acceptable when the predefined consensus level is achieved. Finally, classical examples and simulations are given to demonstrate the effectiveness of the proposed approaches.
机译:当使用加性偏好关系(APR)寻求可靠且令人满意的群体决策(GDM)问题的解决方案时,个人一致性和群体共识都非常重要。在本文中,提出了两种新的算法来促进达成共识的过程,第一种算法用于提高个体的一致性水平,第二种算法旨在帮助小组达到预定义的共识水平。与以前的GDM关于一致性和共识建立的研究不同,所提出的算法本质上是启发式的,仅修改APR中的某些元素以减少一致性和共识过程中偏好修改的次数,并且修改了属于原始评估规模的偏好使生成的建议更易于理解。特别地,共识算法确保当达到预定义的共识级别时,个人一致性级别仍然可以接受。最后,给出了经典的例子和仿真来证明所提方法的有效性。

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