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Journal articles on the topic 'Fuzzy decision making'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

Dinagar, D. Stephen, and A. Nagoorgani. "Fuzzy decision making." Scientific Transactions in Enviornment and Technovation 2, no. 1 (2008): 4–8. http://dx.doi.org/10.20894/stet.116.002.001.002.

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2

D, Latha. "Decision Making in Incomplete Information System with Fuzzy Decision Attributes." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 44–47. http://dx.doi.org/10.21275/sr24830101538.

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V.Anusuya and B.Nisha. "Type-2 Fuzzy Soft Sets on Fuzzy Decision Making Problems." International Journal of Fuzzy Mathematical Archive 13, no. 01 (2017): 09–15. http://dx.doi.org/10.22457/ijfma.v13n1a2.

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Making decision is one of the most fundamental activities of human being. Decision making is a study of how decisions are actually made better. Applications of fuzzy sets within the field of decision making consisted of fuzzifications of the classical theories of decision making. Decisions are made under conditions of uncertainty is the prime domain for fuzzy decision making. In this paper, we have applied the notion of similarity measure and inclusion measure between Type-2 fuzzy soft sets to verify their relationships. This relation is used to obtain a solution of a decision making problem.

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Kahraman, Cengiz. "Fuzzy decision-making applications." International Journal of Approximate Reasoning 44, no. 2 (2007): 91–92. http://dx.doi.org/10.1016/j.ijar.2006.07.002.

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Uzhga-Rebrov, Oļegs. "DECISION MAKING DECISION MAKING BASED ON FUZZY PREFERENCE RELATIONS." ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 8, 2025): 359–66. https://doi.org/10.17770/etr2025vol2.8570.

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Many fuzzy versions of common multi-criteria decision making (MCDM) methods have been proposed to date. Among these methods, a special place is occupied by the method based on fuzzy preference relations (FPR). This method is fuzzy in nature and has no crisp analogue. The essence of the method is to evaluate preferences on pairs of alternatives. The source for evaluation is subjective judgments of expert specialists based on their knowledge and experience. The purpose of this article is to present in detail and clearly the theoretical foundations of this specific method in the context of multi-criteria decision making under conditions of highly uncertain initial information. Based on the initial assignments of the experts, using relevant computational procedures, the resulting preference scores for each of the alternatives are determined. These resulting scores are the basis for selecting the optimal alternative or ranking the alternatives by preference. The article presents two alternative versions of this method. The article presents three illustrative examples, whose purpose is to demonstrate the relevant computational procedures.

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Kumar, Kamesh, and M. K. Sharma. "Generalized Fuzzy Technique and its Consistent Assessment in Multicriteria Decision Making of Medical Decisions." Indian Journal Of Science And Technology 17, no. 42 (2024): 4438–48. http://dx.doi.org/10.17485/ijst/v17i42.3115.

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Objectives: The purpose of this study is to develop a decision-making expert system to assist the diagnostic decisions effectively. Generalized fuzzy sets (Vague sets) are used to model the uncertainty that exists in the process. Method: Initially, concepts of centroid and signed distance are generalized for Trapezoidal Vague Numbers (TVNs). This model introduces some desirable properties for the proposed distance measure. A multicriteria decision-making (MCDM) is introduced using trapezoidal vague numbers (TVNs) and other intuitions in medical diagnostics. In this MCDM, firstly, each symptom is classified into some criteria. Patients’ states and weights for the existing symptoms are assumed as vague point entries. As a mathematical tool, the present model used vague relations for illustrating different associations like symptom criteria and diseases and the state of the patients. The present model also discusses confidence interval-based statistical analysis for the construction of TVNs, which are the prominent component of the study. Findings: A numerical computation is illustrated with the whole procedure of the decision-making. To show the capability and distinctness of the proposed methodology, a comparative discussion for outcomes is given. The present method coincides with the existing fuzzy-based method for the initial diagnosis, while possibility degrees of diagnoses differences for patient P1 and patient P2 differ. It ranges from 1% to 16.3% in the method based on the existing traditionally fuzzy approach, and in the present approach, these differences range from 2.15% to 7.46%. Novelty: This research contributes to presenting a novel decision expert system that is more capable of making diagnosis efficient. This study provides a practical and visual tool to assess potential outcomes of the proposed technique through a numerical example. Keywords: Vague set, Lower and upper membership function, Vague point, Trapezoidal vague number, Medical diagnosis

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Zaliluddin, Dadan. "Bibliometric Analysis of “Accuracy of Multi Criteria Decision Making (MCDM) of Assistance Recipients with Fuzzy Logic Algorithm”." West Science Interdisciplinary Studies 1, no. 07 (2023): 329–39. http://dx.doi.org/10.58812/wsis.v1i07.82.

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The issue of making decisions accurately and swiftly is crucial in the present and is bolstered by an abundance of data; therefore, making correct decisions can save the future. With a large amount of data and numbers, however, the decision-making process will become even more muddled if the statistical ranking values are identical. Therefore, a method is required to determine whether a hazy decision becomes clearer or a decision that is nearly identical is the best. The method used has existed for more than 50 years, and it is fuzzy logic. In the selection of fuzzi, the term Multi-Criteria Decision Making (MCDM) is frequently used, and it continues to be used and expanded. As a result, the increasing number of articles that contain information about Fuzzy Logic Multi-Criteria Decision Making (MCDM) can be used as research material using Bibliometric analysis based on the Scopus. With Bibliometric analysis, tens of thousands of related articles can be analyzed and displayed with VOSviewer software using a variety of categories including authors, titles, citations, updates, and other information to demonstrate the most recent direction of future research on fuzzy logic Multi Criteria Decision Making (MCDM).

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Wang, Liya, та Yu-Ru Syau. "Fuzzy Φ-convexity and fuzzy decision making". Computers & Mathematics with Applications 47, № 10-11 (2004): 1697–705. http://dx.doi.org/10.1016/j.camwa.2004.06.022.

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9

Song, Qian, Abraham Kandel, and Moti Schneider. "Parameterized fuzzy operators in fuzzy decision making." International Journal of Intelligent Systems 18, no. 9 (2003): 971–87. http://dx.doi.org/10.1002/int.10124.

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10

Barach, P., V. Levashenko, and E. Zaitseva. "Fuzzy Decision Trees in Medical Decision Making Support Systems." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 8, no. 1 (2019): 37–42. http://dx.doi.org/10.1177/2327857919081009.

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Fuzzy decision trees represent classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise, conflict, and missing information. Decision Making Support Systems are used widely in clinical medicine because decisions play an important role in diagnostic processes. Decision trees are a very suitable candidate for induction of simple decision-making models with the possibility of automatic learning. The goal of this paper is to demonstrate a new approach for predictive data mining models in clinical medicine. This approach is based on induction of fuzzy decision trees. This approach allows us to build decision-making modesl with different properties (ordered, stability etc.). Three new types of fuzzy decision trees (non-ordered, ordered and stable) are considered in the paper. Induction of these fuzzy decision trees is based on cumulative information estimates. Results of experimental investigation are presented. Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Using new approaches based on fuzzy decision trees allows to increase the prediction accuracy. Decision trees are a very suitable candidate for induction using simple decision-making models with the possibility of automatic and AI learning.

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Seen, Xie Shone, Darvishi Mondragon Ortiz-Barrios, and Osei Scott Kant. "A novel stochastic fuzzy decision model for optimizing decision-making in the manufacturing industry." International Journal of Enterprise Modelling 17, no. 1 (2023): 15–23. http://dx.doi.org/10.35335/emod.v17i1.69.

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In unpredictable and imprecise production environments, this research introduces a stochastic fuzzy decision model for the manufacturing industry. Decision-makers can use the stochastic and fuzzy logic model to capture uncertainties, variability, and language representations of industrial factors. The choice problem, fuzzy input variables, and crisp outcome variables are identified to start the research. Linguistic terms related with fuzzy input variables are represented by fuzzy sets and membership functions. Fuzzy rules link fuzzy input variables to crisp output variables based on expert knowledge or historical data. Objective function, restrictions, and fuzzy rules are incorporated into the stochastic fuzzy decision model's mathematical formulation. Decision-makers can maximize outcomes by considering stochastic factors and fuzzy logic with the model. The model uses an optimization technique to find the optimal choice variable values. A numerical example of manufacturing production planning illustrates the model's use. The results show that the stochastic fuzzy decision model may minimize production costs by calculating optimal production quantities depending on demand. The research concludes that the proposed approach helps manufacturing companies make decisions. Decision-makers can use the model to make educated judgments despite uncertainties and inaccurate information. Future study will explore additional aspects and integrate the model into decision support systems or industrial software. In dynamic and uncertain manufacturing contexts, the stochastic fuzzy decision model empowers manufacturing decision-makers to make optimal decisions

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12

K. Karuppiah. "An Application of Fuzzy Relation Equation and Fuzzy Km-Subalgebras in Decision Making." Panamerican Mathematical Journal 35, no. 3s (2025): 581–93. https://doi.org/10.52783/pmj.v35.i3s.4366.

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In the economic world, it is most important to make a right decision. In our life , we ought to make decisions such as where to live, choosing major course to study, where to work etc… There arises a situation of confused state. In that situation, decision making can be dealt well with the subject Mathematics. In particularly, the concept of fuzzy leads to various approaches in decision making. We use the concept of Fuzzy relation equation and Fuzzy KM-Subalgebras to deal the decision making problems. We have developed an algorithm using the concept of Fuzzy relation equation and a methodology using Fuzzy KM-Subalgebras and also the demonstration is provided. Of these two, we concluded the optimum method for making decisions.

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13

Carlos R. Alcantud, José. "Fuzzy Techniques for Decision Making." Symmetry 10, no. 1 (2017): 6. http://dx.doi.org/10.3390/sym10010006.

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Kusumadewi, Sri, and Idham Guswaludin. "Fuzzy Multi-Criteria Decision Making." Media Informatika 3, no. 1 (2005): 25–38. http://dx.doi.org/10.20885/informatika.vol3.iss1.art3.

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15

Konečný, Vladimír. "Decision making in fuzzy environment." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 55, no. 6 (2007): 227–32. http://dx.doi.org/10.11118/actaun200755060227.

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This paper is attended on simple method for support decision process in their final stage, i.e. by selection of suitable variant, from a set acceptable variant and by using fuzzy sets. In this entry are presented two approaches to solution: one without acceptance of weights point of view selection and second with acceptance weights point of view selection, including a method easy determination weights view points.Approaches are documented with sample decision making about choice new product for extension of produce. As a view points ware selected: costs realization, time realization, employment increase, benefit, environmental impact.

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16

Syau, Yu-Ru, and E. Stanley Lee. "Preincavity and fuzzy decision making." Fuzzy Sets and Systems 155, no. 3 (2005): 408–24. http://dx.doi.org/10.1016/j.fss.2005.01.015.

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17

Santamarina, J. C., and J. L. Chameau. "Decision making and fuzzy windows." International Journal of Approximate Reasoning 2, no. 2 (1988): 107–8. http://dx.doi.org/10.1016/0888-613x(88)90094-1.

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18

Degani, Rosanna, and Giovanni Bortolan. "Fuzzy decision-making in electrocardiography." Artificial Intelligence in Medicine 1, no. 2 (1989): 87–91. http://dx.doi.org/10.1016/0933-3657(89)90020-1.

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19

Saaty, Thomas L., and Liem T. Tran. "Fuzzy Judgments and Fuzzy Sets." International Journal of Strategic Decision Sciences 1, no. 1 (2010): 23–40. http://dx.doi.org/10.4018/jsds.2010103002.

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Using fuzzy set theory has become attractive to many people. However, the many references cited here and in other works, little thought is given to why numbers should be made fuzzy before plunging into the necessary simulations to crank out numbers without giving reason or proof that it works to one’s advantage. In fact it does not often do that, certainly not in decision making. Regrettably, many published papers that use fuzzy set theory presumably to get better answers were not judged thoroughly by reviewers knowledgeable in both fuzzy theory and decision making. Buede and Maxwell (1995), who had done experiments on different ways of making decisions, found that fuzzy does the poorest job of obtaining the right decision as compared with other ways. “These experiments demonstrated that the MAVT (Multiattribute Value Theory) and AHP (Analytic Hierarchy Process) techniques, when provided with the same decision outcome data, very often identify the same alternatives as ‘best’. The other techniques are noticeably less consistent with the Fuzzy algorithm being the least consistent.”

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20

Deepa, S. "Applications of Fuzzy Sets in the Area of Group Decision for N – Person Game." Asian Journal of Computer Science and Technology 1, no. 2 (2012): 45–46. http://dx.doi.org/10.51983/ajcst-2012.1.2.1702.

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Applications of fuzzy sets within the field of decision making were based on the extensions of the classical theories of decision making. Decision making may include any choice or selection of alternatives and is therefore of prime importance in many fields of science. Many managerial decisions are however made with some uncertainty. In case of decision making under conditions of risk or uncertainty more than one states of nature exists, and probabilities or fuzzy decision theories are used to deal with fuzziness. Therefore the quality of the decisions may get diverted from the actual decisional aspects. This paper proposes a set of guidelines based on fuzzy model to be applied for game theories and also work out fuzzy to crisp conversion on the analyzed group decision sets.

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ANZILLI, LUCA. "A POSSIBILISTIC APPROACH TO INVESTMENT DECISION MAKING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, no. 02 (2013): 201–21. http://dx.doi.org/10.1142/s0218488513500116.

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The concept of possibilistic mean value and variance of fuzzy numbers has been applied to investment decisions by using a nonlinear type of fuzzy numbers called adaptive fuzzy numbers. In this paper, by extending the notion of adaptive fuzzy number, we propose a more flexible methodology. Our aim is to allow decision maker more flexibility in dealing with ambiguity and uncertainty. To illustrate the use of our approach and its ability in dealing with ambiguity and imprecision we analyze, as an application, the fuzzy net present value of future cash flows and give some numerical results.

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22

Račić, Željko V. "Fuzzification - Decision Making in Terms of Uncertainty." ECONOMICS 6, no. 2 (2018): 87–94. http://dx.doi.org/10.2478/eoik-2018-0022.

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Summary The theory of fuzzy sets allows to analyze insufficiently precise, accurate, complete phenomena which can not be modeled by the theory of probability or interval mathematics. We define fuzzy sets as sets where the boundary of the set is unclear and depends on subjective estimation or individual preference. In addition to the standard interpretation scale, described above, a set of numbers to each qualitative attribute must be assigned. In addition to the standard interpretation scale a set of numbers to each qualitative attribute must be assigned. First of all, it is necessary to determine the procedure for determining fuzzy numbers describing the attributes. One of the imperfections of the fuzzy sets is subjectivism when defining the boundaries of fuzzy sets and functions of belonging, which can significantly influence the final decision. The decision maker’s subjectivity is also present in the determination of weighted coefficients. However, in case of giving weight, fixed values are necessary. Some decisions require multidisciplinary knowledge, so the decision-making process includes more group decision-makers, who independently give their grades.

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Ye, Dajun, Decui Liang, and Pei Hu. "Three-Way Decisions with Interval-Valued Intuitionistic Fuzzy Decision-Theoretic Rough Sets in Group Decision-Making." Symmetry 10, no. 7 (2018): 281. http://dx.doi.org/10.3390/sym10070281.

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In this article, we demonstrate how interval-valued intuitionistic fuzzy sets (IVIFSs) can function as extended intuitionistic fuzzy sets (IFSs) using the interval-valued intuitionistic fuzzy numbers (IVIFNs) instead of precision numbers to describe the degree of membership and non-membership, which are more flexible and practical in dealing with ambiguity and uncertainty. By introducing IVIFSs into three-way decisions, we provide a new description of the loss function. Thus, we firstly propose a model of interval-valued intuitionistic fuzzy decision-theoretic rough sets (IVIFDTRSs). According to the basic framework of IVIFDTRSs, we design a strategy to address the IVIFNs and deduce three-way decisions. Then, we successfully extend the results of IVIFDTRSs from single-person decision-making to group decision-making. In this situation, we adopt a grey correlation accurate weighted determining method (GCAWD) to compute the weights of decision-makers, which integrates the advantages of the accurate weighted determining method and grey correlation analysis method. Moreover, we utilize the interval-valued intuitionistic fuzzy weighted averaging (IIFWA) operation to count the aggregated scores and the accuracies of the expected losses. By comparing these scores and accuracies, we design a simple and straightforward algorithm to deduce three-way decisions for group decision-making. Finally, we use an illustrative example to verify our results.

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Kisworo, Kisworo. "FMADM: YAGER MODEL IN FUZZY DECISION MAKING." Jurnal Tekno Kompak 12, no. 1 (2018): 1. http://dx.doi.org/10.33365/jtk.v12i1.43.

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This paper presents Yager model, i.e. standard form of Fuzzy Multi-Atributte Decision Making (FMADM) in fuzzy decision environment. Simulasion of this model would be performed under scope of fuzzy decisionmaking process to show its existence. As academics, researchers, and practitioners know on it, besides theFMADM, so is there Fuzzy Multi-Objective Decision Making (FMODM) at where the both has their same derivation, e.i. Fuzzy Multi-Criteria Decision Making (FMCDM). Related to the matter, significant value that could be represented then gives contribution to team work-oriented principal of decision makers.Keywords: Yager Model, FMADM, FMODM, FMCDM, fuzzy decision-making.

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Voskoglou, Michael Gr. "Grey Multiple-Criteria Decision-Making." International Journal of Grey Systems 4, no. 1 (2024): 5–10. http://dx.doi.org/10.52812/ijgs.88.

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Decision Making (DM) is one of the most important components of human cognition. In particular, the Multiple-Criteria DM (MCDM), is a composite form of DM evaluating options with conflicting goals and choosing the best solution among the existing ones. Following the fuzzy DM criterion of Bellman and Zadeh in 1970, several other methods have been developed by other researchers for DM in fuzzy environments. Here we present a parametric, MCDM method utilizing grey numbers as tools. This method improves an earlier approach of Maji and colleagues in 2002, who used the tabular representation of a soft set as a tool for parametric MCDM in a fuzzy environment. The method is also extended to cover cases of weighted DM and suitable examples are presented illustrating our results.

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Zhu, Kuanyun, and Jianming Zhan. "Fuzzy parameterized fuzzy soft sets and decision making." International Journal of Machine Learning and Cybernetics 7, no. 6 (2015): 1207–12. http://dx.doi.org/10.1007/s13042-015-0449-z.

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Wirsam, B., A. Hahn, EO Uthus, and C. Leitzmann. "Fuzzy sets and fuzzy decision making in nutrition." European Journal of Clinical Nutrition 51, no. 5 (1997): 286–96. http://dx.doi.org/10.1038/sj.ejcn.1600378.

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28

Hyacenth, Chineke Amaechi. "Optimizing Multiple Object Tracking and Decision making using neuro–fuzzy." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 871–76. http://dx.doi.org/10.31142/ijtsrd23185.

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Gaur, Vibha, and Anuja Soni. "An Integrated Approach to Prioritize Requirements using Fuzzy Decision Making." International Journal of Engineering and Technology 2, no. 4 (2010): 320–28. http://dx.doi.org/10.7763/ijet.2010.v2.141.

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Jaleesha, B. K., and Dr S. Shenbaga Ezhil. "Interval Valued Fuzzy Sets in Decision Making and Optimization Models." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1290–97. http://dx.doi.org/10.5373/jardcs/v11sp10/20192974.

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Malasowe, B. O., and A. Emuobonuvie. "Modelling Entrepreneurial Decision Making Process Using Fuzzy Inference Systems." Advances in Multidisciplinary and scientific Research Journal Publication 29 (December 15, 2021): 99–110. http://dx.doi.org/10.22624/aims/abmic2021-v2-p8.

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The essence of Entrepreneurship is making decisions that are constantly required to evaluate alternatives and make decisions regarding a wide range of matters. Decision making involves a lot of uncertainty and risk which poses serious challenges for Entrepreneurs to joggle among them in other to take the best decision. Study have shown that focus has been mainly on the analysis of the characteristics of potential entrepreneurs and the firm-creation process leaving the critical incredient that will help in decision making. The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Hence, this paper proposes a decision support model -Fuzzy Inference System- that could be adapted for various business decision processes. This system has the capability to handle decision making, by critically considering the degree of membership of all the risk involved in a given problem space. The model uses the concept of fuzzy set theory to judiciously select the variables in a given problem space in an uncertain situations. From a real life practical point of view, this theory offers a natural approach to the resolution of multidimensional and complex problems when the available information is sparse and/or of poor quality. The fuzzy rule base sytem soften the adverse effects that a business may suffer from these uncertain factors. Keywords: Fuzzy logic, Membership functions, Decision, Fuzzy Set, Entrepreneur

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NAZARI-SHIRKOUHI, S., A. ANSARINEJAD, SS MIRI-NARGESI, V. MAJAZI DALFARD, and K. REZAIE. "INFORMATION SYSTEMS OUTSOURCING DECISIONS UNDER FUZZY GROUP DECISION MAKING APPROACH." International Journal of Information Technology & Decision Making 10, no. 06 (2011): 989–1022. http://dx.doi.org/10.1142/s0219622011004683.

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During the last decade, information system (IS) outsourcing has emerged as a major issue for organizations. As outsourcing decisions are often based on multicriteria approaches and group decisions, this paper proposes a structured methodology based on Fuzzy group decision making approach to evaluate and select the appropriate information system project (ISP) in an actual case. To achieve our purpose, we argue that seven criteria consisting of risk, management, economics, technology, resource, quality, and strategy and five ISPs should be considered for outsourcing decisions. Fuzzy analytic hierarchy process (fuzzy AHP) to find the priority and ranking of each ISP is considered. Using fuzzy theory for selecting the proper IS project can reduce ambiguities and uncertainties that are inherent in the selection procedure. A sensitivity analysis is performed to check the steadiness of the priority ranking and help decision makers to understand different scenarios that show alternative potential developments or different viewpoints concerning the relative importance of the criteria. Our proposed methodology can be applied by a cross-functional team of practitioners and IT managers to select the proper IS project. Also, the proposed approach can effectively consider complex and qualitative decision variables involved in the IS outsourcing decision-making problem. Finally, conclusion and potential issues for future research are presented. The main findings of this research have showed that the combination of AHP method and fuzzy concept to rank and prioritize of IS projects to outsource is a useful and practical tool to make traceable and reliable decision. According to results, a priority of overall score for the five candidates of IS projects reflects that project of "development of the supplier relationship management information system" performs the best, and project of "facilities management" performs the worst according to the experts' judgment in case study.

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Zhang, Jun Ling, and Xiao Wen Qi. "Research on Multiple Attribute Decision Making under Hesitant Fuzzy Linguistic Environment with Application to Production Strategy Decision Making." Advanced Materials Research 753-755 (August 2013): 2829–36. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2829.

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This paper investigates methods for multiple attribute decision making (MADM) under hesitant fuzzy linguistic environment. Firstly, we define two transform functions between hesitant fuzzy linguistic variables and hesitant fuzzy variables. Then, based on the presented transform functions, a hesitant fuzzy linguistic weighted averaging (HFLWA) operator and a hesitant fuzzy linguistic weighted geometric (HFLWG) operator are developed, and some desired properties of the operators are also analyzed. Successively, an integrated approach for MADM with attribute assessments taking form of hesitant fuzzy linguistic variables is constructed. Furthermore, an illustrative study on production strategy decision making is carried out to verify the effectiveness and practicality of proposed methods.

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Valaskova, Katarina, Viera Bartosova, and Pavol Kubala. "Behavioural Aspects of the Financial Decision-Making." Organizacija 52, no. 1 (2019): 22–31. http://dx.doi.org/10.2478/orga-2019-0003.

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Abstract Background and Purpose: Behavioural finance is a relatively new, but rapidly evolving field that provides explanations of an economic decision-making by cognitive psychology, conventional economic and financial theory. Behavioural finance searches the influence of psychology on the behaviour of financial practitioners and the subsequent effects on the financial markets. The purpose of the paper is the research on behavioural aspects of financial decision-making as they help explain why and how markets might be inefficient. Design/Methodology/Approach: Fuzzy logic is an excellent tool for working with linguistic variables that are often found when working with behavioural data. Thus, we analyse the financial decision-making process from the perspective of behavioural finance aimed at better understanding of the decision-making process of investors applying the principles of fuzzy logic to solve various financial problems. Results: The results of the study indicate that fuzzy logic is applicable when solving problems of financial management and financial decision-making problems. The urgency of the fuzzy logic application for managerial and financial decisions should be emphasized. Research in this area indicates that in some cases, as in the case of behavioural financing, the use of fuzzy logic is far more suitable than the use of other methods (Peters, Aguiar and Sales). Conclusion: The novelty of the paper is to extend the application of fuzzy sets in the area of financial decision-making. The paper demonstrates that despite the fact, that fuzzy logic is currently used mainly in technical directions, it is applicable also in financial management, especially, in cases where it is necessary to consider the influence of human and the occurrence of linguistic variables.

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Liu, Qi, and Jianming Zhan. "IFP-Intuitionistic Fuzzy Softh-Ideals of Hemirings and Its Decision Making." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/589465.

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The aim of this paper is to introduce the concepts of IFP-intuitionistic fuzzy softh-ideals and IFP-equivalent intuitionistic fuzzy softh-ideals of hemirings. Some characterizations and properties of them are given. In particular, some good examples are explored. Finally, we investigate aggregate intuitionistic fuzzyh-ideals of hemirings based on decision making.

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Agayev, F. B. "Analytical support of the procedural decision-making process." Mathematical machines and systems 4 (2020): 20–32. http://dx.doi.org/10.34121/1028-9763-2020-4-20-32.

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In the process of legal research, private scientific methods are developed and used to study legal reality, such as the method of comparative jurisprudence, the method of interpretation (explanation) and the formal legal method. However, at the present stage of legal research, it is impossible to be limited only by these methods. Even legal scholars who consistently defend the status of dogmatic jurisprudence recognize that the application of these methods, with all their merits, sets a limiting framework in un-derstanding the practical action of positive law and the originality of its theoretical vision. Nevertheless, the application of these methods in the study of legal reality allows us to draw a conclusion about the general trends in the development of evaluative concepts that are fundamental in civil procedural law. It is offered an approach to the formation of a system of information support of procedural decision-making based on the application of fuzzy inference mechanism implemented in the logical basis of the feedforward multilayer neural network. Under this approach, a method to overcome the semantic uncer-tainty in the evaluation terms of procedural law is developed by using appropriate terms (fuzzy sets) of corresponding linguistic variables. As an example it is selected the Articles on “Violation of copyright or neighboring rights” of the Criminal Code of the Azerbaijan Republic based on which has been proposed formalism for the evaluation concept of “significant harm” in relation to the sanction applied. For mak-ing an adequate to evaluation concept notion it is proposed grading scale of possible sanctions, obtained on the basis of the description of the relevant legal norms in terms of fuzzy implicative rules.

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YU, P. L., and C. I. CHIANG. "DECISION MAKING, HABITUAL DOMAINS AND INFORMATION TECHNOLOGY." International Journal of Information Technology & Decision Making 01, no. 01 (2002): 5–26. http://dx.doi.org/10.1142/s0219622002000063.

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In this paper, we discuss how information technology (IT) affects and influences people to make decisions. We first introduce human behavior mechanism and habitual domains — the software that drive the behaviors. Then we discuss the impacts of IT on decision elements and environment, and then IT's impacts on a variety of decision problems including routine problems, mixed routine problems, fuzzy problems and challenging problems. IT is useful in solving routine problems but not as obvious in solving fuzzy and challenging problems. To solve fuzzy and challenging problems, an effective concept and model of competence set analysis is introduced. Finally, we describe three types of competence set analysis and show how IT can help in these three types of problems.

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Dai, Jianhua, Guojie Zheng, Qinghua Hu, Maofu Liu, and Huashi Su. "Decision-theoretic rough set approach for fuzzy decisions based on fuzzy probability measure and decision making." Journal of Intelligent & Fuzzy Systems 31, no. 3 (2016): 1341–53. http://dx.doi.org/10.3233/ifs-162200.

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Fathi, V. A., A. P. Ganzhur, M. A. Ganzhur, N. V. Dyachenko, and R. M. Shabanov. "Decision-making system with homo and hetero elements." Journal of Physics: Conference Series 2131, no. 2 (2021): 022120. http://dx.doi.org/10.1088/1742-6596/2131/2/022120.

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Abstract Experts from all over the world provide an opportunity in filling decision-making systems. But the filling of decision-making systems with data does not have an exact quantitative characteristic. It is good when the expert is completely confident in the decision. But some decisions can add up to their own internal assessment without justification or experimentation. Other decisions are hampered by past experience. To overcome this type of problem, it is necessary to develop a system that will be based on clear and fuzzy data behaviour. This article is aimed at describing the method for constructing a decision-making system on clear and fuzzy data using Petri nets.

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Huang, Hai Bin, Li Na Cai, and Peng Chao Cai. "Dual Hesitant Fuzzy Information Aggregation in Decision Making." Applied Mechanics and Materials 389 (August 2013): 854–59. http://dx.doi.org/10.4028/www.scientific.net/amm.389.854.

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As an extension of hesitant fuzzy sets, dual hesitant fuzzy sets can depict the attitude when decision makers give their certain or uncertain hesitant fuzzy evaluation information. In this paper, we develop the dual hesitant fuzzy ordered weighted averaging (DHFOWA) operator and the dual hesitant fuzzy ordered weighted geometric (DHFOWG) operator, study their properties, utilize them to aggregate dual hesitant fuzzy decision making information and verify their effectiveness and correctness.

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Abosuliman, Shougi S., Saleem Abdullah, and Muhammad Qiyas. "Three-Way Decisions Making Using Covering Based Fractional Orthotriple Fuzzy Rough Set Model." Mathematics 8, no. 7 (2020): 1121. http://dx.doi.org/10.3390/math8071121.

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On the basis of decision-theoretical rough sets (DTRSs), the three-way decisions give new model of decision approach for deal with the problem of decision. This proposed model of decision method is based on the loss function of DTRSs. First, the concept of fractional orthotriple fuzzy β -covering (FOF β -covering) and fractional orthotriple fuzzy β -neighborhood (FOF β -neighborhood) was introduced. We combined loss feature of DTRSs with covering-based fractional orthotriple fuzzy rough sets (CFOFSs) under the fractional orthotriple fuzzy condition. Secondly, we proposed a new FOF-covering decision-theoretical rough sets model (FOFCDTRSs) and developed related properties. Then, based on the grade of positive, neutral and negative membership of fractional orthotriple fuzzy numbers (FOFNs), five methods are established for addressing the expected loss expressed in the form of FOFNs and the corresponding three-way decisions are also derived. Based on this, we presented a FOFCDTRS-based algorithm for multi-criteria decision making (MCDM). Then, an example verifies the feasibility of the five methods for solving the MCDM problem. Finally, by comparing the results of the decisions of five methods with different loss functions.

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Padder, Riyaz Ahmad, Taghreed Alqurashi, Yasir Rather, and Shilpa Malge. "Interval Valued Spherical Fuzzy Matrix in Decision Making." European Journal of Pure and Applied Mathematics 18, no. 2 (2025): 6095. https://doi.org/10.29020/nybg.ejpam.v18i2.6095.

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Recent advancements have demonstrated the potential to augment matrix theory with fuzzy, intuitionistic fuzzy, picture fuzzy, interval-valued picture fuzzy matrix concepts for enhanced decision-making applications. We introduce the interval valued spherical fuzzy matrix, extending the spherical fuzzy matrix, to effectively represent and manipulate uncertain and vague information with enhanced flexibility. This paper establishes definitions and theorems for Interval-Valued spherical fuzzy matrices. We develop methods for computing determinant and adjoint, and develop algorithms using composition functions to determine the greatest and least eigenvalue interval valued spherical fuzzy sets and create a flow chart to depict the procedure. In this paper, a new distance measure has been proposed and is to be proved valid by satisfying all the conditions of the distance metric. In addition, an application of interval-valued spherical fuzzy matrices to deal with decision-making problems is presented.

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Zhang, Mo, Qinghua Zhang, and Man Gao. "A sequential three-way decision model based on hesitant fuzzy sets." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 9073–84. http://dx.doi.org/10.3233/jifs-201524.

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As a new extended model of fuzzy sets, hesitant fuzzy set theory is a useful tool to process uncertain information in decision making problems. The traditional hesitant fuzzy multi-attribute decision making (MADM) can only choose an optimal strategy, which is not suitable for all of the complex scenarios. Typically, in practical application, decision making problems may be more complicated involving three options of acceptance, non-commitment and rejection decisions. Three-way decisions, which divide universe into three disjoint regions by a pair of thresholds, are more efficient to deal with these problems. Therefore, how to utilize three-way decision theory to process hesitant fuzzy information is an essential issue to be studied. In this paper, from the perspective of hesitant fuzzy distance, a hesitant fuzzy three-way decision model is proposed. First, because hesitant fuzzy element (HFE) is a set of several possible membership degrees, it cannot be compared with thresholds directly. Hence, this paper converts it into the comparison between the distance and the thresholds. Then, to calculate thresholds more reasonably, shadowed set theory is introduced to avoid the subjectivity of threshold acquisition. Furthermore, sequential strategy is adopted to solve the multi-attribute decision making problems. Finally, an example of medical diagnosis and simulation experiments are given to prove the accuracy and efficiency of the proposed hesitant fuzzy three-way decision model.

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Marimuthu, G., and G. Ramesh. "On Moderate Fuzzy Analytic Hierarchy Process Pairwise Comparison Model With Subcriteria." International Research Journal of Engineering, IT & Scientific Research 2, no. 3 (2016): 54. http://dx.doi.org/10.21744/irjeis.v2i3.41.

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Decisions usually involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques. Every decision making can be considered as the choice from the set of alternatives based on a set of criteria. The fuzzy analytic hierarchy process is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons mode [10]. The weight vectors from this comparison model are obtained by using extent analysis method. This paper concern with an alternate method of finding the weight vectors from the original fuzzy AHP decision model (moderate fuzzy AHP model), that has the same rank as obtained in original fuzzy AHP and ideal fuzzy AHP decision models.

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Nigar Hüseynova, Nigar Hüseynova. "DECISION MAKING BASED ON NATURAL LANGUAGE PROCESSING." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 10, no. 06 (2021): 87–92. http://dx.doi.org/10.36962/pahtei1006202187.

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The processing of natural language, one of the main areas of artificial intelligence, has begun to be widely used in decision-making. The use of natural language processing plays a crucial role in improving decision making. Currently, natural language processing technology allows us to achieve better results in some accurate decision-making technologies. Because people understand linguistic information better and can make better decisions based on it. It is easier for people to understand the meaning of linguistic terms than quantitative information. Many studies have shown that people have difficulty in understanding quantitative information or are reluctant to make decisions based on it. The main indicator of its effectiveness is the fact that the level of reliability of decision-making based on linguistic information, which is closer to human intuition, is higher than that based on other types of information. The article discusses the measurement of natural language decision-making efficiency. In order to measure the number of tourists who can travel to the three selected countries in three quarters, people were provided with natural language processing and graphical surveys. Based on the collected data, fuzzy inference analysis was applied. The survey found that people tend to make decisions based on natural language rather than the most up-to-date schedules. Only 27% of people made decisions based on graphs, while others made decisions based on the “If ... Then” rule. The reliability of decisions made through natural language processing was 78%, while the other was 62%. Keywords: Natural Language Processing, Natural Language Generation, Decision Making based on Uncertainty, Fuzzy Rules, Fuzzy Inference Systems, Deep Learning.

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Ramya, Thangavel, A. C. Kannan, R. S. Balasenthil, and B. Anusuya Bagirathi. "Fuzzy Logic Modeling for Decision Making Processes Using MATLAB." Advanced Materials Research 984-985 (July 2014): 425–30. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.425.

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— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.

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Gong, Zengtai, and Junhu Wang. "Hesitant fuzzy graphs, hesitant fuzzy hypergraphs and fuzzy graph decisions1." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 865–75. http://dx.doi.org/10.3233/jifs-201016.

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Up to now, there have been a lot of research results about multi-attribute decision making problems by fuzzy graph theory. However, there are few investigations about multi-attribute decision making problems under the background of indecisiveness. The main reason is that the difference of cognition and the complexity of thinking by decision makers, for the same question have different opinions. In this paper, we proposed a hesitant fuzzy hypergraph model based on hesitant fuzzy sets and fuzzy hypergraphs. At the same time, some basic graph operations of hesitant fuzzy hypergraphs are investigated and several equivalence relationship between hesitant fuzzy hypergraphs, hesitant fuzzy formal concept analysis and hesitant fuzzy information systems are discussed. Since granular computing can deal with multi-attribute decision-making problems well, we considered the hesitant fuzzy hypergraph model of granular computing, and established an algorithm of multi-attribute decision-making problem based on hesitant fuzzy hypergraph model. Finally an example is given to illustrate the effectiveness of the algorithm.

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YOSHIDA, Yuji, and Masami YASUDA. "Decision-Making in Dynamic Fuzzy Systems." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 6 (1998): 1053–62. http://dx.doi.org/10.3156/jfuzzy.10.6_1053.

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SUGIYAMA, Takao, and Hisao SHIIZUKA. "Decision Making by Hierarchical Fuzzy Integrals." Journal of Japan Society for Fuzzy Theory and Systems 5, no. 4 (1993): 772–82. http://dx.doi.org/10.3156/jfuzzy.5.4_772.

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Solanki, Rinki, Q. M. Danish Lohani, and Pranab K. Muhuri. "Probabilistic Intuitionistic Fuzzy Decision Making Algorithms." IEEE Access 9 (2021): 99651–66. http://dx.doi.org/10.1109/access.2021.3095521.

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