Target Threat Assessment Based On Interval-valued Intuitionistic Fuzzy Multi-attribute Decision Making With Dynamic Fusion

Target threat assessment is an important part of realizing modern battlefield assistant decision-making,as well as the premise and key to complete firepower distribution scientifically and reasonably.Generally speaking,target threat assessment is a multi-attribute decision problem essentially.On one hand,the knowledge and ability of decision makers are limited,which makes it more difficult to describe the fuzzy uncertainty of decision information accurately.On the other hand,the traditional target threat assessment methods mainly focus on the data at single moment,which cannot reflect the change of the target situations.Therefore,it is necessary to analyze the threat of targets within multiple times merging.In a conclusion,to address the problems above,this paper,relying on the equipment development project,has made further study on the dynamic target threat assessment based on the dynamic multi-attribute decision-making,involving the power weighted geometric average(PWGA)operator of IVIFN,the generation of attribute weights and time weights.The main tasks we have done are as follows:(1)A multi-attribute decision making method based on the new proposed dynamic power weighted geometric average(PWGA)operator of IVIFN is proposed.As respect to the traditional geometric average operator in dynamic multi-attribute decision making method with integrated data expressed in interval-valued intuitionistic fuzzy number(IVIFN)fails to consider the relationships between the integrated data,consequently,this degrades the overall precision of the final decision result.Therefore,the ability to display nonmonotonic behavior provides one of the useful features of PWGA operator to establish a connection between the integrated data.A numerical example is given to illustrate and validate the proposed approaches.As a result,we can conclude that it does make full use of the collected information of the targets.(2)Solution to the attribute weights based on a new maximum deviation optimization model is proposed.As for the dynamic multi-attribute decision making problem with the attribute weights unknown,a new maximum deviation optimization model is proposed to obtain attribute weights based on IVIF distance,which makes the attribute weights more accord with the fact.Besides,using the new approach to derive the attribute weights can also reduce the influence of attributes weights reassigned irrationally.(3)Acquisition of time weights based on the quantized time preference with similarity measure is introduced.As the time factor is often ignored in some situations involved time dependent MADM problems,a new approach is bound to appear to address this aspect.In this method,we constructed a multi-target optimization programming model,which fused time degree theory that was technique for order preference by similarity to ideal solution(TOPSIS)based on subjective preference and information entropy principle based on objective preference,to obtain time weight.The feasibility and significance of the presented method over existing methods were verified via analysis of numerical examples.