Dynamic nonlinear simplified neutrosophic sets for multiple-attribute group decision making

作     者：Qiu, Junda Jiang, Linjia Fan, Honghui Li, Peng You, Congzhe 

作者机构：Jiangsu Univ Technol Sch Comp Engn Changzhou 213001 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China 

出 版 物：《HELIYON》 (Heliyon)

年 卷 期：2024年第10卷第5期

页      面：e27493页

核心收录：

基　　金：National Natual Science Foundation of China [62002142, 61902160] Natural Science Foundation of the Jiangsu Higher Education Institutions of China [20KJD520002, 19KJB520006] Natural Science Foundation of Jiangsu Province [BK20201057] 

主　　题：Dynamic nonlinear simplified neutrosophic set Aggregation model Multiple -attribute group decision making Decision algorithm TOPSIS 

摘      要：In this paper, the concept of a dynamic nonlinear simplified neutrosophic set (DNSNS) is proposed for describing the real-time changing expert preference information. Furthermore, the DNSNS aggregation model and decision algorithm are provided to solve the actual multiple-attribute group decision making (MAGDM) problems. The basic notions, the similarity measure, the entropy measure, and the index of distance of DNSNS are presented first. Secondly, the univariate time series of DNSNS are projected into dynamic nonlinear simplified neutrosophic curves in three-dimensional space. The areas of the surface enclosed by the curves represent the variance among the DNSNSs. Thus, the DNSNS aggregation model is established correctly without preprocessing the original data. Afterward, the aggregation algorithm extended from the plant growth simulation algorithm (PGSA) is proposed for calculating the optimal aggregation preference curve and constructing the collective matrix. Additionally, a novel corresponding decision algorithm based on TOPSIS and projection theory is proposed for obtaining the overall ranking of alternatives in the actual MAGDM problem. Finally, a typical example is presented to illustrate the feasibility and effectiveness of the proposed model and algorithm.