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首页> 外文期刊>Applied Soft Computing >An aggregation method for solving group multi-criteria decision-making problems with single-valued neutrosophic sets
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An aggregation method for solving group multi-criteria decision-making problems with single-valued neutrosophic sets

机译:用单值中性组求解组多标准决策问题的聚合方法

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We develop a novel method that uses single-valued neutrosophic sets (NSs) to handle independent multi source uncertainty measures affecting the reliability of experts' assessments in group multi-criteria decision-making (GMCDM) problems. NSs are characterized by three independent membership magnitudes (falsity, truth and indeterminacy) and can be employed to model situations characterized by complex uncertainty. In the proposed approach, the neutrosophic indicators are defined to explicitly reflect DMs' credibility (voting power), inconsistencies/errors inherent to the assessing process, and DMs' confidence in their own evaluation abilities. In contrast with most of the existing studies, single-valued NSs are used not only to formalize the uncertainty affecting DMs' priorities, but also to aggregate them into group estimates without the need to define neutrosophic decision matrices or aggregation operators. Group estimates are synthesized into crisp evaluations through a two-step deneutrosophication process that converts (1) single-valued NSs in fuzzy sets (FSs) using the standard Euclidean metric and (2) FSs in representative crisp values using defuzzification. Theoretical and practical implications are discussed to highlight the flexibility of the proposed approach. An illustrative example shows how taking into account the uncertainty inherent to the experts' evaluations may deeply affect the results obtained in a standard fuzzy environment even when dealing with very simple ranking problems. (c) 2018 Elsevier B.V. All rights reserved.
机译:我们开发一种新颖的方法,采用单值中控器(NSS)来处理影响专家对组多标准决策(GMCDM)问题的专家评估可靠性的独立多源不确定性措施。 NSS的特点是三个独立的会员量大(虚假,真理和不确定),并且可以用于模拟特征的模拟局势不确定性。在拟议的方法中,中性学指标被定义为明确反映DMS的可信度(投票权),评估过程中固有的不一致/错误,以及DMS对自己评估能力的信心。与大多数现有研究相比,单值NSS不仅用于正式化影响DMS优先级的不确定性,而且还要将它们聚合成组估计,而无需定义中性学决策矩阵或聚合运营商。组估计通过两步的DeneutroOpoction过程合成为清晰的评估,该过程在使用Defuzzzification的代表性清晰值中使用标准欧几里德度量和(2)FSS在模糊集(FSS)中转换(1)单值NSS。讨论了理论和实践意义以突出所提出的方法的灵活性。一个说明性的例子显示了如何考虑到专家的不确定性,即使在处理非常简单的排名问题时也可能深入影响标准模糊环境中获得的结果。 (c)2018 Elsevier B.v.保留所有权利。

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