计算系统因果标准权重的改进混合目标模型:基于DEMATEL和DEVELOPED SWARA(D-DS)的模型

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Seyed Hossain Ebrahimi
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引用次数: 0

摘要

准则加权是多准则决策问题的一个重要特征,在工程、计算机科学和管理研究中得到了广泛的应用。特别是在许多与复杂系统有关的研究中,通常会有两组主要的因果标准。在本研究中,旨在建立一个混合目标模型,包括DEMATEL和SWARA技术,为因果标准的子组分配分类权重。作为主要目标,本文提出的混合模型可以为属于原因组的标准分配更大的值。在这方面,我们应用从参数(R,等于一个标准的直接和间接影响的总和)、(R/C,称为一个标准的净影响力)和(R-C,称为一个标准的净效应)衍生的客观信息,这些参数与DEMATEL方法中的最终总影响矩阵T相关。本工作的主要贡献在于利用了SWARA方法,并对其进行了修订,其中SWARA技术中应用的相对比较重要性Sj通过一些聚合算子(包括max, Einstein和Hamacher算子)重新配置,以获得相对于SWARA基本方法更均匀的因果标准权重。结果表明,(R/C)和(R-C)传递的数据和数值信息更清晰、精细化,获得的因果两组标准权重更好、可靠性更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Modified Hybrid Objective Model to Calculate the Weights of Cause and Effect Criteria in a System: DEMATEL and DEVELOPED SWARA (D-DS) Based Model
Abstract Criteria weighting is a widely used and also an important feature of multi criteria decision making problems specially in engineering, computer science and management investigations. In particular in many studies related to complex systems there would be usually two main groups of cause and effect criteria. In this research it is intended to make an hybrid objective model comprising DEMATEL and SWARA techniques to assign classified weights to the subgroup of cause and effect criteria. As a main goal, the proposed hybrid model in this presented paper can afford to assign greater values for criteria who belong to cause group. In this regard we apply the objective information which derived from the parameters of (R, equal to sum of direct and indirect influence of a criteria), (R/C, named as net influence power of a criteria) and (R-C, named as net effect of a criteria) related to the final total influence matrix T in DEMATEL methodology. The main contribution in this work lies in utilizing the SWARA methodology and making us of its revision where the relatively Comparative Importance Sj, applied in SWARA technique is reconfigured by some aggregation operators including max, Einstein and Hamacher operators for obtaining more uniformed weights of cause and effect criteria relatively to SWARA basic methodology. Finally results shows that the (R/C) and (R-C)would transfer more clear and refined data and numeric information achieving better and highly reliable weights of criteria categorized into two groups of cause and effect group.
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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