Fuzzy Logic in Defense Management Activities (original) (raw)
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2008 IEEE International Conference on Systems, Man and Cybernetics, 2008
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Evaluating Weapons System Using Fuzzy Approximate Reasoning
The International Conference on Mathematics and Engineering Physics, 2010
This paper proposed the application of the fuzzy evaluation model using fuzzy sets and approximate reasoning. The objective of the study is to evaluate the weapon system in a subjective environment. The proposed method based on fuzzy sets has initiated the idea of membership set score value evaluation of each criterion alternative. This enables the inclusion of requirements which are incomplete and imprecise. The approximate reasoning of the method allows the decision maker to make the best choice in accordance to human thinking and reasoning processes. The proposed model is based on fuzzy multi-criteria decisionmaking that consists of fuzzy rules. The use of fuzzy rules, which are extracted directly from input data in making evaluation, contributes to a better decision in selecting the best option and less dependency on the domain of experts. Finally, we constructed a practical example for evaluating attack helicopters to demonstrate the proposed method. From these results, the proposed method shows outstanding performance in comparing with Cheng et al.'s method with 100% accuracy in ranking three attack helicopter alternatives, S1 (MI-28), S2 (AH-64, and S3 (AH-1w). Again the subjective evaluation method showed the advantages of simpler rules properties in NR , Max_L and Min_L. This research work has achieved its objective and produced good evaluation results. This portrays its major advantages in making decisions in new cases, where there is limited or an absence of specific knowledge.
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International Journal of Intelligent Information Technologies, 2016
Data for military intelligence operations are increasing at astronomical rates. As a result, significant cognitive and temporal resources are required to determine which information is relevant to a particular situation. Soft computing techniques, such as fuzzy logic, have recently been applied toward decision support systems to support military intelligence analysts in selecting relevant and reliable data within the military decision making process. This article examines the development of one such system and its evaluation using a constructive simulation and human performance model to provided critical understanding of how this conceptual information system might interact with personnel, organizational, and system architectures. In addition, similarities between military intelligence analysts and cyber intelligence analysts are detailed along with a plan for transitioning the current fuzzy-based system to the cyber security domain.
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This paper proposed to apply fuzzy sets and approximate reasonings to evaluate the weapons system. The objective of the study is to determine the ranking of the weapons system in a subjective environment. The proposed model based on fuzzy sets has initiated the idea of membership set score value evaluation of each criterion alternative. This enables the inclusion of requirements which are incomplete and imprecise. The approximate reasonings of the method allows the decision maker to make the best choice in accordance to human thinking and reasoning processes. The proposed model is based on fuzzy multi-criteria decision-making that consists of fuzzy rules. The use of fuzzy rules, which are extracted directly from input data in making evaluation, contributes to a better decision in selecting the best choice and is less dependent on the domain of expert. The dataset from previous research was used to validate the fuzzy evaluation model. Results from numerical examples are comparable to other fuzzy evaluation approaches.
A Fuzzy MCDM Framework for Weapon Systems Selection
Advances in Logistics, Operations, and Management Science, 2019
The weapon system selection problem is a crucial issue for military logistics managers and decision makers. In most real-world cases, such critical selection problems include many alternatives, and those alternatives have to be assessed with respect to multiple criteria. In this chapter, the use of multi-criteria decision making (MCDM) approaches to tackle the weapon selection problem is discussed. Next, a fuzzy MCDM framework that is based on the hierarchical fuzzy technique for order preference by similarity to ideal solution (HFTOPSIS) method is proposed to solve the problem. The proposed approach is capable of incorporating both crisp and fuzzy data. The authors demonstrate the performance of the proposed methodology on a missile system selection problem which incorporates fuzzy environment elements.
Journal of the Operational Research Society, 2013
In this study, a model representing military requirements as scenarios and capabilities is offered. Pair-wise comparisons of scenarios are made according to occurrence probabilities by using the Analytical Hierarchy Process (AHP). The weights calculated from AHP are used as the starting weights in a Quality Function Deployment (QFD) matrix. QFD is used to transfer war fighter requirements into the benefit values of projects. Two levels of QFD matrices are used to evaluate new capability areas versus capabilities and capabilities versus projects. The benefit values of the projects are used in a multiobjective problem (multi-objective multiple knapsack problem) that considers the project benefit, implementation risks and environmental impact as multiple objectives. Implementation risk and environmental impact values are also calculated using the same combined AHP and QFD methodology. Finally, the results of the fuzzy multi-objective goal programming suggest a list of projects that offers optimal benefit when carried out within multiple budgets.
DIBR–DOMBI–FUZZY MAIRCA Model for Strategy Selection in the System of Defense
Discrete Dynamics in Nature and Society
Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–“DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA meth...
Criteria evaluations by means of fuzzy logic: Case study: The cost of a nuclear-fuel repository
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In this chapter a fuzzy methodology is presented for the evaluation of criteria for which the level of available knowledge is limited or inexistent. Due to this lack of knowledge, statistical techniques and data mining are only useable in a limited way. The criteria evaluations rely on the elicitation of experts' knowledge. For coping with criteria evaluations in a similar uncertainty context a three-tiered fuzzy inference system has been developed. Details on the fuzzy rules and implications in this inference system are provided. This approach has been used in practice for the cost and financial analysis of radioactive-waste management. The case study on budgeting a nuclear-fuel repository is a simplified version of a real project assessment. It is presented along with the FIS software, which has been developed for the analysis. It is thought that the approach can be extended to different criteria evaluations, particularly in the field of environmental management.
A fuzzy decision support system for the economic calculus in radioactive waste management
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A methodology has been elaborated to derive contingency factors for evaluating in a realistic way the costs of first-of-the-kind projects in radioactive waste management (RWM). The paper describes the practical implementation of the fuzzy decision support system (FDSS) and its interface to assist economists in charge of the economic calculus. Uncertainties to be added on top of basic cost evaluations are represented by two contingency factors, respectively, called the P- and the T-factors. The P-factor represents the uncertainties of the project induced by its still incomplete advancement; the T-factor represents the uncertainties caused by the still insufficient technological maturity on which the project is based. Progressive implication rules of the Goguen type are used with the two contingency factors as outputs. Input variables for P and T are given on relative [0,1] scales. Fuzzy logic is also used as front-end for obtaining the two inputs in the course of peer reviews of technology-experts and project-specialists. To that aim, the semantic opinions and past experience of the latter are expressed in the form of conditional and unconditional rules. The credibility of T-experts and P-specialists are taken into account by using the Kleene–Dienes inference. A numerical example on the cost of disposal of high-level radioactive waste in a deep repository is used to illustrate the practical use of the FDSS. This approach is also applicable to other economic assessments inside or outside the nuclear field.