A linear programming aggregation method based on generalized Zhenyuan integral in q-ROFN environment and the application of talent recruitment in universities
IF 7.2 1区 计算机科学Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0
Abstract
The reasonable ranking of binary pairs that characterize fuzzy information in many fuzzy decision problems is very important. To overcome some defects of the existing score functions for the q-rung orthopair fuzzy numbers (q-ROFNs), a novel score function and ranking criterion are proposed by the q-compression transformation and hesitation factor. The main motivation is to introduce the generalized Zhenyuan (GZ)-integral into the q-ROFN environment, and cleverly transform the aggregation operations into a linear programming problem through the arithmetic operations of q-ROFNs. The main contribution is to solve the aggregation problem of q-rung orthopair fuzzy generalized Zhenyuan integral ordered weighted average (q-ROFGZIOWA) operator through the optimization technique of linear programming, and a new decision making method is established by using the q-ROFGZIOWA operator and ranking criterion. The main innovation is to map all q-ROFNs to the unit triangle in the first quadrant (converted into intuitionistic fuzzy numbers, IFNs) according to the q-compression transformation in geometric significance, and the novel score function and its ranking criterion are proposed by combining hesitation factor, and then the aggregation operation based on generalized Z-integral is converted to an optimization problem in linear programming. Finally, the superiority of the proposed method are verified by comparing the aggregation results of two integral operators through an example, and apply the proposed method to the optimal decision-making of talent recruitment in universities. The proposed method can not only correct some flaws in the ranking of existing q-ROFNs, but also overcomes some defects of existing Choquet integral average (geometric) operators in a q-ROFN environment. These results are of great significance for further research on the widespread application of q-ROFNs.
期刊介绍：
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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