Relevant bibliographies by topics / Fuzzy decision making

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fuzzy decision making'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Fuzzy decision making"

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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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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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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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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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Dissertations / Theses on the topic "Fuzzy decision making"

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Musikasuwan, Salang. "Novel fuzzy techniques for modelling human decision making." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/13161/.

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Standard (type-1) fuzzy sets were introduced to resemble human reasoning in its use of approximate information and uncertainty to generate decisions. Since knowledge can be expressed in a more natural by using fuzzy sets, many decision problems can be greatly simplified. However, standard type-1 fuzzy sets have limitations when it comes to modelling human decision making. In many applications involving the modelling of human decision making (expert systems) the more traditional membership functions do not provide a wide enough choice for the system developer. They are therefore missing an opportunity to produce simpler or better systems. The use of complex non-convex membership functions in the context of human decision making systems were investigated. It was demonstrated that non-convex membership functions are plausible, reasonable membership functions in the sense originally intended by Zadeh. All humans, including ‘experts’, exhibit variation in their decision making. To date, it has been an implicit assumption that expert systems, including fuzzy expert systems, should not exhibit such variation. Type-2 fuzzy sets feature membership functions that are themselves fuzzy sets. While type-2 fuzzy sets capture uncertainty by introducing a range of membership values associated with each value of the base variable, but they do not capture the notion of variability. To overcome this limitation of type-2 fuzzy sets, Garibaldi previously proposed the term ‘non-deterministic fuzzy reasoning’ in which variability is introduced into the membership functions of a fuzzy system through the use of random alterations to the parameters. In this thesis, this notion is extended and formalised through the introduction of a notion termed a non-stationary fuzzy set. The concept of random perturbations that can be used for generating these non-stationary fuzzy sets is proposed. The footprint of variation (FOV) is introduced to describe the area covering the range from the minimum to the maximum fuzzy sets which comprise the non-stationary fuzzy sets (this is similar to the footprint of uncertainty of type-2 sets). Basic operators, i.e. union, intersection and complement, for non-stationary fuzzy sets are also proposed. Proofs of properties of non-stationary fuzzy sets to satisfy the set theoretic laws are also given in this thesis. It can be observed that, firstly, a non-stationary fuzzy set is a collection of type-1 fuzzy sets in which there is an explicit, defined, relationship between the fuzzy sets. Specifically, each of the instantiations (individual type-1 sets) is derived by a perturbation of (making a small change to) a single underlying membership function. Secondly, a non-stationary fuzzy set does not have secondary membership functions, and secondary membership grades. Hence, there is no ‘direct’ equivalent to the embedded type-2 sets of a type-2 fuzzy sets. Lastly, the non-stationary inference process is quite different from type-2 inference, in that non-stationary inference is just a repeated type-1 inference. Several case studies have been carried out in this research. Experiments have been carried out to investigate the use of non-stationary fuzzy sets, and the relationship between non-stationary and interval type-2 fuzzy sets. The results from these experiments are compared with results produced by type-2 fuzzy systems. As an aside, experiments were carried out to investigate the effect of the number of tunable parameters on performance in type-1 and type-2 fuzzy systems. It was demonstrated that more tunable parameters can improve the performance of a non-singleton type-1 fuzzy system to be as good as or better than the equivalent type-2 fuzzy system. Taken as a whole, the techniques presented in this thesis represent a valuable addition to the tools available to a model designer for constructing fuzzy models of human decision making.
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Chuang, Poon-Hwei. "Fuzzy mathematical programming in civil engineering systems." Thesis, Imperial College London, 1985. http://hdl.handle.net/10044/1/7802.

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Foroughi, Behzad. "Decision-making in manufacturing systems, an IPA/fuzzy approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38629.pdf.

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Siddique, Muhammad. "Fuzzy decision making using max-min and MMR methods." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3042.

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Fuzzy logic is based on the theory of fuzzy sets, where an object’s membership of a set is gradual rather than just member or not a member. Fuzzy logic uses the whole interval of real numbers between zero (False) and one (True) to develop a logic as a basis for rules of inference. Particularly the fuzzified version of the modus ponens rule of inference enables computers to make decisions using fuzzy reasoning rather than exact. We study decision making problem under uncertainty. we analyze Max-Min method and Minimization of regret method originally developed by Savage and further developed by Yager. We generalize The MMR method by creating the parameterized family of minimum regret methods by using the ordered weighted averaging OWA operators.
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Jaffal, Hussein, and Cheng Tao. "Multiple Attributes Group Decision Making by Type-2 Fuzzy Sets and Systems." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2659.

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We are living in a world full of uncertainty and ambiguity. We usually ask ourselves questions that we are uncertain about their answers. Is it going to rain tomorrow? What will be the exchange rate of euro next month? Why, where and how should I invest? Type-1 Fuzzy sets are characterized by the membership function whose value for a given element x is said to be the grade of membership having a value in the interval [0, 1]. In addition, type-1 fuzzy sets have limited capabilities to deal with uncertainty. In our thesis, we study another concept of a fuzzy description of uncertainty which is called Type-2 fuzzy sets. According to this concept, for any given element x, we can’t speak of an unambiguously specified value of the membership function. Moreover, Type-2 fuzzy sets constitute a powerful tool for handling uncertainty. The aim of our thesis is to examine the potential of the Type-2 fuzzy sets especially in decision making. So, we present basic definitions concerning Type-2 fuzzy sets, and operations on these sets are to be discussed too. Then, Type-2 fuzzy relations and methods of transformation of Type-2 fuzzy sets will be introduced. Also, the theory of Type-2 Fuzzy sets will serve for the construction of the fuzzy inference system. Finally, we utilize interval type-2 fuzzy sets in the application of Multiple Attributes Group Decision Making which is called TOPSIS.
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Strasser, Mark. "The development of a fuzzy decision-support system for dairy cattle culling decisions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29794.pdf.

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Levy, Bat-Sheva. "Fuzzy logic, a model to explain students' mathematical decision-making." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0026/MQ51391.pdf.

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Oderanti, Festus Oluseyi. "Fuzzy decision making system and the dynamics of business games." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2446.

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Effective and efficient strategic decision making is the backbone for the success of a business organisation among its competitors in a particular industry. The results of these decision making processes determine whether the business will continue to survive or not. In this thesis, fuzzy logic (FL) concepts and game theory are being used to model strategic decision making processes in business organisations. We generally modelled competition by business organisations in industries as games where each business organization is a player. A player formulates his own decisions by making strategic moves based on uncertain information he has gained about the opponents. This information relates to prevailing market demand, cost of production, marketing, consolidation efforts and other business variables. This uncertain information is being modelled using the concept of fuzzy logic. In this thesis, simulation experiments were run and results obtained in six different settings. The first experiment addresses the payoff of the fuzzy player in a typical duopoly system. The second analyses payoff in an n-player game which was used to model a perfect market competition with many players. It is an extension of the two-player game of a duopoly market which we considered in the first experiment. The third experiment used and analysed real data of companies in a case study. Here, we chose the competition between Coca-cola and PepsiCo companies who are major players in the beverage industry. Data were extracted from their published financial statements to validate our experiment. In the fourth experiment, we modelled competition in business networks with uncertain information and varying level of connectivity. We varied the level of interconnections (connectivity) among business units in the business networks and investigated how missing links affect the payoffs of players on the networks. We used the fifth experiment to model business competition as games on boards with possible constraints or restrictions and varying level of connectivity on the boards. We also investigated this for games with uncertain information. We varied the level of interconnections (connectivity) among the nodes on the boards and investigated how these a ect the payoffs of players that played on the boards. We principally used these experiments to investigate how the level of availability of vital infrastructures (such as road networks) in a particular location or region affects profitability of businesses in that particular region. The sixth experiment contains simulations in which we introduced the fuzzy game approach to wage negotiation in managing employers and employees (unions) relationships. The scheme proposes how employers and employees (unions) can successfully manage the deadlocks that usually accompany wage negotiations. In all cases, fuzzy rules are constructed that symbolise various rules and strategic variables that firms take into consideration before taken decisions. The models also include learning procedures that enable the agents to optimize these fuzzy rules and their decision processes. This is the main contribution of the thesis: a set of fuzzy models that include learning, and can be used to improve decision making in business.
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Hinojosa, William. "Probabilistic fuzzy logic framework in reinforcement learning for decision making." Thesis, University of Salford, 2010. http://usir.salford.ac.uk/26716/.

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This dissertation focuses on the problem of uncertainty handling during learning by agents dealing in stochastic environments by means of reinforcement learning. Most previous investigations in reinforcement learning have proposed algorithms to deal with the learning performance issues but neglecting the uncertainty present in stochastic environments. Reinforcement learning is a valuable learning method when a system requires a selection of actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems with reinforcement learning, the environment is considered deterministic. However, for many cases, the consequence of an action may be uncertain or stochastic in nature. This work proposes a novel reinforcement learning approach combined with the universal function approximation capability of fuzzy systems within a probabilistic fuzzy logic theory framework, where the information from the environment is not interpreted in a deterministic way as in classic approaches but rather, in a statistical way that considers a probability distribution of long term consequences. The generalized probabilistic fuzzy reinforcement learning (GPFRL) method, presented in this dissertation, is a modified version of the actor-critic learning architecture where the learning is enhanced by the introduction of a probability measure into the learning structure where an incremental gradient descent weight- updating algorithm provides convergence. XXIABSTRACT Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: first, the GPFRL have shown a robust performance when used in control optimization tasks. Second, its learning speed outperforms most of other similar methods. Third, GPFRL agents are feasible and promising for the design of adaptive behaviour robotics systems.
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Chen, Zhifeng. "Consensus in group decision making under linguistic assessments." Diss., Manhattan, Kan. : Kansas State University, 2005. http://hdl.handle.net/2097/68.

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Books on the topic "Fuzzy decision making"

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Li, Hong-Xing. Fuzzy sets and fuzzy decision-making. CRC Press, 1995.

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Pedrycz, Witold, Petr Ekel, and Roberta Parreiras. Fuzzy Multicriteria Decision-Making. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470974032.

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Kahraman, Cengiz, and Özgür Kabak, eds. Fuzzy Statistical Decision-Making. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7.

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Chen, Shu-Jen, and Ching-Lai Hwang. Fuzzy Multiple Attribute Decision Making. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46768-4.

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Skalna, Iwona, Bogdan Rębiasz, Bartłomiej Gaweł, et al. Advances in Fuzzy Decision Making. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26494-3.

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Lai, Young-Jou, and Ching-Lai Hwang. Fuzzy Multiple Objective Decision Making. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-57949-3.

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Kahraman, Cengiz, ed. Fuzzy Multi-Criteria Decision Making. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76813-7.

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author, Huang Jih-Jeng, ed. Fuzzy multiple objective decision making. CRC Press, Taylor & Francis Group, 2014.

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Kontogiorgos, Lambros. Decision making in fuzzy environment. UMIST, 1998.

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Hosseinzadeh Lotfi, Farhad, Tofigh Allahviranloo, Witold Pedrycz, Mohammadreza Shahriari, Hamid Sharafi, and Somayeh Razipour GhalehJough. Fuzzy Decision Analysis: Multi Attribute Decision Making Approach. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-44742-6.

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Book chapters on the topic "Fuzzy decision making"

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Lee, E. Stanley, and Hsu-shih Shih. "Fuzzy Decision Making." In Fuzzy and Multi-Level Decision Making. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0683-8_5.

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Arora, Rishu, Gagandeep Kaur, and Kamal Kumar. "Fuzzy decision-making." In Strategic Fuzzy Extensions and Decision-making Techniques. CRC Press, 2024. http://dx.doi.org/10.1201/9781003497219-1.

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Torğul, Belkız, and Turan Paksoy. "Fuzzy Decision-Making." In Smart and Sustainable Operations and Supply Chain Management in Industry 4.0. CRC Press, 2023. http://dx.doi.org/10.1201/9781003180302-16.

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Altay, Ayca, and Didem Cinar. "Fuzzy Decision Trees." In Fuzzy Statistical Decision-Making. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7_13.

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Erginel, Nihal, and Sevil Şentürk. "Fuzzy EWMA and Fuzzy CUSUM Control Charts." In Fuzzy Statistical Decision-Making. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7_15.

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Grabisch, Michel, Hung T. Nguyen, and Elbert A. Walker. "Decision Making." In Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8449-4_7.

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Kahraman, Cengiz, and Özgür Kabak. "Fuzzy Statistical Decision-Making." In Fuzzy Statistical Decision-Making. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7_1.

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Carlsson, Christer, and Robert Fullér. "Fuzzy Multicriteria Decision Making." In Fuzzy Reasoning in Decision Making and Optimization. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1805-5_2.

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Basaran, Murat Alper, Biagio Simonetti, and Luigi D’Ambra. "Fuzzy Correlation and Fuzzy Non-linear Regression Analysis." In Fuzzy Statistical Decision-Making. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7_12.

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Sarı, İrem Uçal, Cengiz Kahraman, and Özgür Kabak. "Fuzzy Dispersion Measures." In Fuzzy Statistical Decision-Making. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39014-7_6.

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Conference papers on the topic "Fuzzy decision making"

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Kumar, Vikram, Dimple Saini, Shailesh Rastogi, Ramakrishnan Raman, Apurv Verma, and R. Meenakshi. "Hybrid Fuzzy Decision-Making Model." In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2024. http://dx.doi.org/10.1109/icacite60783.2024.10616910.

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Madi, Elissa Nadia, and Azwa Abdul Aziz. "Enhancing Fuzzy Decision-Making Models with an Extended Interval-Valued Comparison." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612157.

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Marhamati, Nina, Elham S. Khorasani, and Shahram Rahimi. "Bayesian decision making using z-numbers." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737972.

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Vershinina, L. P. "OBJECTIFICATION OF FUZZY INFERENCE IN DECISION-MAKING PROBLEMS." In MODELING AND SITUATIONAL MANAGEMENT THE QUALITY OF COMPLEX SYSTEMS. Saint Petersburg State University of Aerospace Instrumentation, 2021. http://dx.doi.org/10.31799/978-5-8088-1558-2-2021-2-5-7.

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The basis of modern decision support systems is not so much analytical and statistical models as the practical application of specialists ‘ knowledge. Such systems are based on fuzzy technologies. The quality of decisions made depends on how accurately the quality of information is reflected in the fuzzy inference process. Ways to improve the objectivity of fuzzy inference at the stages of fuzzification, aggregation, activation, and accumulation are proposed.
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Agarwal, Manish, Madasu Hanmandlu, and Kanad K. Biswas. "New linguistic aggregation operators for decision making." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891679.

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Homenda, Wladyslaw, Agnieszka Jastrzebska, Witold Pedrycz, Fusheng Yu, and Yihan Wang. "Multicriteria Decision Making: Scale, Polarity, Symmetry, Interpretability." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177705.

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Gherasim, Ovidiu. "Fuzzy Models in Decision-Making." In 2008 International Conference on Computational Intelligence for Modelling Control & Automation. IEEE, 2008. http://dx.doi.org/10.1109/cimca.2008.228.

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Alcantud, Jose Carlos R., and Gustavo Santos-Garcia. "Expanded hesitant fuzzy sets and group decision making." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015758.

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Rezaei, A. A., R. Mohammadifar, E. Lotfi, A. Khosravi, and S. Nahavandi. "A heterogeneous defense method using fuzzy decision making." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015761.

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Kacprzyk, Janusz, Slawomir Zadrozny, Hannu Nurmi, and Alexander Bozhenyuk. "Towards innovation focused fuzzy decision making by consensus." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494531.

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Reports on the topic "Fuzzy decision making"

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Karam, Sofia, Morteza Nagahi, Vidanelage Dayarathna, Junfeng Ma, Raed Jaradat, and Michael Hamilton. Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41026.

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The emergence of modern complex systems is often exacerbated by a proliferation of information and complication of technologies. Because current complex systems challenges can limit an organization's ability to efficiently handle socio-technical systems, it is essential to provide methods and techniques that count on individuals' systems skills. When selecting future employees, companies must constantly refresh their recruitment methods in order to find capable candidates with the required level of systemic skills who are better fit for their organization's requirements and objectives. The purpose of this study is to use systems thinking skills as a supplemental selection tool when recruiting prospective employees. To the best of our knowledge, there is no prior research that studied the use of systems thinking skills for recruiting purposes. The proposed framework offers an established tool to HRM professionals for assessing and screening of prospective employees of an organization based on their level of systems thinking skills while controlling uncertainties of complex decision-making environment with the fuzzy linguistic approach. This framework works as an expert system to find the most appropriate candidate for the organization to enhance the human capital for the organization.
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Buyak, Bogdan B., Ivan M. Tsidylo, Victor I. Repskyi, and Vitaliy P. Lyalyuk. Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2669.

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The article describes the problem of designing a neuro-fuzzy expert system of professional selection at the stages of conceptualization and formalization, which involves the definition of concepts, relationships and management mechanisms necessary to describe the solution of problems in the chosen subject field. The structural model of the decision making system for determining the professional selection of students for training in IT specialties is substantiated. Three subsystems are proposed as structural components for studying: psychological peculiarities, personal qualities, factual knowledge, abilities and skills of students. The quality of the system’s operation is determined by the use of various techniques for acquiring knowledge on the basis of which the knowledge base of the neuro-fuzzy system and the combination of the use of fuzzy and stochastic data will be formed.
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Sperry, Richard. Multi-Perspective Technology Assessment to Improve Decision Making: A Novel Approach Using Fuzzy Cognitive Mapping for a Large-Scale Transmission Line Upgrade. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.1821.

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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.
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