Distance, similarity and entropy measures of dynamic interval-valued neutrosophic soft sets and their application in decision making

In this paper, we introduce the notion of dynamic interval-valued neutrosophic soft sets (DIVNSSs) by embedding the time factor to interval-valued neutrosophic soft sets (IVNSSs). We also present some related set theoretic operations, such as complement, union, intersection, and-product, and or-prod...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 12; no. 7; pp. 2007 - 2025
Main Authors Dong, Yuanxiang, Cheng, Xiaoting, Hou, Chenjing, Chen, Weijie, Shi, Hongbo, Gong, Ke
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2021
Springer Nature B.V
Subjects
Artificial Intelligence
Comparative analysis
Complex Systems
Computational Intelligence
Control
Decision analysis
Decision making
Engineering
Entropy
Fuzzy sets
Mechatronics
Nonlinear programming
Original Article
Parameter sensitivity
Pattern Recognition
Robotics
Sensitivity analysis
Set theory
Similarity
Systems Biology
Time factors
Time series
Inconsistent information
DIVNSSs
Decision making
Time factor
Soft sets
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Summary:In this paper, we introduce the notion of dynamic interval-valued neutrosophic soft sets (DIVNSSs) by embedding the time factor to interval-valued neutrosophic soft sets (IVNSSs). We also present some related set theoretic operations, such as complement, union, intersection, and-product, and or-product. Then, we propose the information measures of DIVNSSs, including the distance, similarity, and entropy measures. And we develop three corresponding decision making methods. In the decision making process, we employ a nonlinear programming model to weight every single time objectively, considering that the importance degrees of every single time are quite different. Further, we put forward a dynamic interval-valued neutrosophic soft aggregation rule to combine the parameter weights evaluated by all experts under every single time. Moreover, we give a numerical example to display the application of the proposed methods in decision making. Finally, we present a sensitivity analysis of the parameter time-degree and a comparative analysis with the methods of IVNSSs and interval-valued neutrosophic sets (IVNSs). The results show the effectiveness and superiority of the proposed method in solving the problem with dynamic inconsistent information.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-021-01289-6