基于 Aczel-Alsina 算子和 MEREC 的新型中性立方 MADM 方法及其在供应商选择中的应用

Shanshan Zhai, Jianping Fan, Lin Liu
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引用次数: 0

摘要

中值立方集(NCS)可以通过选择区间值和单值成员资格以及不确定性和虚假成分来处理复杂信息。基于 Aczel-Alsina t-norm 和 t-corm 的聚合算子对评估属性间的相互关系非常有效。本文旨在利用中性三次方信息诊断属性间的相互关系,并针对中性三次方环境下权重未知的供应商选择问题提出一种多属性决策(MADM)方法。我们定义了中性立方阿克塞尔-阿尔西纳(NC-AA)算子和中性立方阿克塞尔-阿尔西纳加权算术平均(NCAAWAA)算子,然后讨论了所提算子的各种重要结果和一些特性。此外,我们还提出了一种存在 NC-AAWAA 算子的 MADM 方法。当属性权重未知时,我们使用 MEREC 方法来确定权重。随后,我们应用 NC-AAWAA 算子和 MEREC 方法来解决供应商选择问题。最后,我们进行了敏感性分析和比较分析,以说明所提方法的稳定性和优越性。结果表明,NC-AAWAA 算子能更有效地处理复杂信息之间的相互关系,MEREC 方法能根据中性立方属性的去除效果对属性进行加权。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel neutrosophic cubic MADM method based on Aczel-Alsina operator and MEREC and its application for supplier selection
Neutrosophic cubic set (NCS) can process complex information by choosing both interval value and single value membership and indeterminacy and falsehood components. The aggregation operators based on Aczel-Alsina t-norm and t-corm are quite effective for evaluating the interrelationship among attributes. The purpose of this paper is to diagnose the interrelationship among attributes with neutrosophic cubic information, and propose a multi-attribute decision-making(MADM) method for supplier selection problem with unknown weight under neutrosophic cubic environment. We defined neutrosophic cubic Aczel-Alsina (NC-AA) operator and neutrosophic cubic Aczel–Alsina weighted arithmetic average (NCAAWAA) operator, then we discussed various important results and some properties of the proposed operators. Additionally, we proposed a MADM method under the presence of the NC-AAWAA operator. When the weights of attributes are unknown, we use the MEREC method to determine the weights. Later, the NC-AAWAA operator and MEREC method are applied to address the supplier selection problem. Finally, a sensitivity analysis and a comparative analysis are conducted to illustrate the stability and superiority of the proposed method. The results show the NC-AAWAA operator can handle the interrelationship among complex information more effectively, and MEREC method can weight the attributes based on the removal effect of a neutrosophic cubic attribute.
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