Cited by 2,555Multi-Criteria Decision MakingFuzzy and Soft Set TheoryRough Sets and Fuzzy Logic

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The concept of a linguistic variable and its application to approximate reasoning—II

Zadeh, L.A.·1975·Information Sciences

DOI 10.1016/0020-0255(75)90046-8

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Fuzzy Representation of Principal’s Preferences in Inspire Negotiation Support System
Piasecki, K., Roszkowska, E., Wachowicz, T., Filipowicz-Chomko, M., & Łyczkowska-Hanćkowiak, A. (2021) · Entropy · doi:10.3390/e23080981 · Cited by 2
“In line with Zadeh [41–43], the ratings corresponding to the linguistic terms were represented by fuzzy numbers (FN).”
BackgroundMethod
Granular fuzzy pay-off method for real option valuation
Cabrerizo, F., Heikkilä, M., Mezei, J., Morente-Molinera, J. A., Herrera-Viedma, E., & Carlsson, C. (2020) · Expert systems with applications · doi:10.1016/j.eswa.2020.113597 · Cited by 2
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Terletskyi, D. O., & Provotar, O. I. (2020) · International Conference on Computer Science and Information Technologies · doi:10.1007/978-3-030-63270-0_21 · Cited by 2
A new hybrid supplier selection model
Nguyen, T. S., Mohamed, S., & Rahman, A. (2019) · International Journal of Operations Research · doi:10.1504/ijor.2019.102412 · Cited by 2
“Linguistic terms have been regarded as intuitively easy-to-use concepts in expressing subjective and imprecise assessments of DMs (Zadeh, 1975a, 1975b).”
BackgroundMethod
Why Use a Fuzzy Partition in F-Transform?
Kreinovich, V., Kosheleva, O., & Sriboonchitta, S. (2019) · Axioms · doi:10.3390/axioms8030094 · Cited by 2
The fuzzification issue in the Wu–Mendel approach for linguistic summarisation using IF-THEN rules
Mirzakhanov, V. (2018) · Journal of experimental and theoretical artificial intelligence (Print) · doi:10.1080/0952813x.2018.1544202 · Cited by 2
“Nowadays, most of the existing LS approaches involve some components of Zadeh ’ s fuzzy logic (fuzzy set theory (Zadeh, 1965), linguistic variables (Zadeh, 1975a, 1975b, 1975c), etc.).”
Method
Simulation and evaluation of second-order fuzzy boundary value problems
Otadi, M. (2018) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-018-3604-0 · Cited by 2
“From Zadeh (1975a), A is a convex fuzzy set if and only if its h -level set A h { x ∈ R | μ A ( x ) ≥ h } is a convex set for all h.”
BackgroundMethod
FUZZY THEORY – A SURVEY ON ITS FOUNDATIONS AND ADVANCEMENTS
Zafar, S., Rahman, N., & Tanweer, S. (2018) · doi:10.26483/ijarcs.v9i2.5719 · Cited by 2
“In 1975, Zadeh introduced the concept of a Linguistic Variable [4, 5] for approximate reasoning.”
Background
Linguistic-Valued Logics Based on Hedge Algebras and Applications to Approximate Reasoning
Nguyen, T.-M.-T., & Tran, D.-K. (2017) · doi:10.18576/amis/110509 · Cited by 2
“Subsequently, Zadeh argued that the set of linguistic values of linguistic variables can be regarded as a formal language generated by a context-free grammar and hedges can be viewed as operators on fuzzy sets [28,29, 30].”
Background
Interval type 2 fuzzy sets multiple criteria decision making based on quantifier guided aggregation ordered weighted averaging operator
Chiao, K.-P. (2017) · 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) · doi:10.1109/ifsa-scis.2017.8023334 · Cited by 2
“Based on the extension principle [5], [11], the IT2FS membership for the IT2FS quantifier guided aggregation OWA Operator can be express as”
Background
Fuzzy multinomial logistic regression analysis: A multi-objective programming approach
Abdalla, H. A., El-Sayed, A., & Hamed, R. (2017) · doi:10.1063/1.4982855 · Cited by 2
“Consequently, the fuzzy model is expressed as: where Tj β Tj β is assumed to be a symmetric triangular fuzzy number as defined by Zadeh [17-19] and refined later by Dubois & Prade [4].”
Method
Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review
D'Urso, P. (2017) · Granular Computing · doi:10.1007/s41066-017-0040-y · Cited by 2
A Novel Entropy of Interval Valued Fuzzy Set
Liu, C.-F., & Luo, Y.-S. (2015) · doi:10.2174/1874110x01509012526 · Cited by 2
“With the development of the fuzzy sets, many new theories came out, such as the interval valued fuzzy sets [2, 3, 4] and the generalized theory of uncertainty [5].”
On Neural Fuzzy Systems
Su, S., & Yeh, J.-W. (2014) · International Journal of Fuzzy Logic and Intelligent Systems · doi:10.5391/ijfis.2014.14.4.276 · Cited by 2
“As mentioned in [54-56], those pre-defined membership functions can be modified (or tuned) by some operations, like “very” or “more-or-less”.”
Background
Optimal Manufacturing-Remanufacturing Production Policy for a Closed-Loop Supply Chain under Fill Rate and Budget Constraint in Bifuzzy Environments
Kundu, S., Chakrabarti, T., & Jana, D. (2014) · International Journal of Mathematics and Mathematical Sciences · doi:10.1155/2014/690435 · Cited by 2
Rationality of two fuzzy negotiation solutions by Knowledge Engineering to n-person cooperative games
González, E., Espín, R. A., Mazcorro, G., & Muñoz, S. (2013) · doi:10.2991/.2013.7 · Cited by 2
“…2: Predicates p(i,C) and a(i,C) correspond to the statements "player i is important to coalition C", and "player i has feasible and advantageous alternatives to reach an agreement within C". p(i,C) is squared in order to model the linguistic modifier related to the statement "very", [16].”
Background
Linguistic Variables of Type-N. A Mathematical Model
Santiago, R., & Maeder, C. (2011) · doi:10.5540/tema.2011.012.01.0021 · Cited by 2
“However, the concept of Type-n fuzzy sets [9, 10, 11] leads quickly to the possibility to generalize such systems to Systems of Type-n; namely, systems where the level of uncertainty are Fuzzy Sets of Type-n.”
Notice of RetractionSecond-order difference heuristic model of fuzzy time series based on particle swarm optimization
Chi, K., Che, W.-G., Fu, F.-P., & Zhuo, M.-K. (2010) · International Conference on Computer Engineering and Technology · doi:10.1109/iccet.2010.5485279 · Cited by 2
“Since the fuzzy time series model was first proposed by Song and Chissom [2-4] based on Zadeh’s works [5-8], numerous research have been conducted to improve forecasting accuracy or reduce computational overload.”
Notice of RetractionA Modified Model of Fuzzy Time Series for Forecasting Exchange Rates
Chi, K., Fu, F.-P., & Che, W.-G. (2010) · 2010 Second International Workshop on Education Technology and Computer Science · doi:10.1109/etcs.2010.120 · Cited by 2
“To solve this problem, Song and Chissom [14-16] presented the definition of fuzzy time series by means of fuzzy theory [17-20].”
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A Fuzzy Quantitative Integrated Metric Model for CMMI Appraisal
Cheng, C.-H., Chang, J.-R., Kuo, C.-Y., & Liao, S.-Y. (2009) · Hybrid Artificial Intelligence Systems · doi:10.1007/978-3-642-02319-4_26 · Cited by 2
“A linguistic variable is “a variable whose values are words or sentences in a natural or artificial language” [17,18].”
Background
Neuroinformatics I: Fuzzy Neural Networks of More-Equal-Less Logic (Static)
Kirvelis, D., & Dagytė, G. (2004) · Informatics Educ. · doi:10.15388/infedu.2004.06 · Cited by 2
“Fuzzy logic, evolved out of fuzzy or Zadeh sets several decades ago (Zadeh, 1965), is of special interest, as well as its application to the neural networks (Zadeh, 1975; Bezdek, 1996; Garliauskas, 1998; 2003).”
Background
A New Approach to Three Way Decision Through Spherical Double Hierarchy Linguistic Information
Khan, M. S. A., Abdullah, S., Khan, F., Qadir, A., & Ghani, F. (2024) · IEEE Access · doi:10.1109/access.2023.3302843 · Cited by 2
“To solve this problem, Zadeh [6] presented the linguistic variable (LV) firstly and then Xu [7] extended the discrete linguistic term set (LTS) to the continuous LTS.”
Method
Application of Cube IQ software and multicriteria optimization models for the selection of vehicles for the transport of goods in the Serbian Armed Forces
Đuričić, I. (2023) · Vojnotehnički Glasnik · doi:10.5937/vojtehg71-42298 · Cited by 2
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A novel approach for optimization in a fuzzy finite capacity queuing model with system cost and expected degree of customer satisfaction
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Score Calculation in Informatics Contests Using Multiple Criteria Decision Methods
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Zelewski, S. (1988) · ZOR Methods Model. Oper. Res. · doi:10.1007/bf01920573 · Cited by 2
An Integrated AHP–Fuzzy AHP Evaluation Framework for Large Language Models in Software Engineering Education
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Return as a vague element: fuzzy multi-objective portfolio making under sustainable investment strategy
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“The developed meth - odological approaches are based on the concept of linguistic variables (Zadeh 1975a, b, c ) or fuzzy relations (Zadeh 1971).”
Multi-criteria decision-making with EDAS method and weighted operators for linguistic Fermatean hesitant fuzzy sets
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“Early in the 1970s, Lotfi A. Zadeh introduced it as an expansion of fuzzy sets (FSs) theory (Zadeh 1975a , b , c ).”
Hesitant fuzzy time series forecasting: A novel approach to handle the hesitancy in the system
Bisht, K., Kumar, S., Pant, M., & Negi, S. (2025) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2025.117322 · Cited by 1
Irreversibility of recursive Heaviside memory functions: a distributional perspective on structural cognition
Shin, C. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10346-7 · Cited by 2
“The recursive sigmoid-based model we introduce is inspired by fuzzy logic (Zadeh 1965, 1975a ; 1975b ; 1975c), wherein graded transitions replace binary thresholds—a requirement for modeling the continuity of cognitive phenomena.”
Organization of Interaction of Autonomous Intelligent Robots in the Process of Joint Solution of Complex Subtasks
Melekhin, V., & Khachumov, M. (2025) · Mekhatronika Avtomatizatsiya Upravlenie · doi:10.17587/mau.26.536-546 · Cited by 1
Research on machining performance for electrochemical grinding of high-speed steel roll material based on interval type-2 fuzzy control
Nie, L., Wu, H.-C., Cao, G., & Yuan, K. (2025) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-025-0938-2 · Cited by 1
“Fuzzy control theory [30, 31] was proposed by Professor Zadeh.”
MABAC Model Based on Linguistic (p, q)-Rung Orthopair Fuzzy Z-Number and Their Application in Green Supply Chain Management
Ali, Z., Pamucar, D., Simić, V., & Dhanaraj, R. K. (2025) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2025.10.009 · Cited by 1
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Industrialization in development countries based on the MABAC method and Hamacher power aggregation operators for circular spherical fuzzy 2-tuple linguistic information
Ali, Z., & Yang, M.-S. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02010-8 · Cited by 1
“In 1975, Zadeh [46, 47] introduced the model of linguistic term sets (LTSs).”
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“Zadeh [53] introduces IT2F sets as an alternative to conventional fuzzy sets, allowing for improved handling of uncertainties related to an element’s membership owing to their inherently fuzzy membership function [54].”
Generalized q-rung picture linguistic Schweizer and Sklar aggregation operators and their application in decision making
Ali, J., Khalid, U., Binyamin, M., Syam, M., & Usman, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-92536-1 · Cited by 1
“When making decisions, we don’t select from as many options as we can; instead, we pick the one that best suits our needs, demands, etc. Zadeh 1 established the fuzzy set (FS) theory with the goal of enhancing the adaptability of the assessments that aid in the method of decision-making (DM) 2,3 .”
Proportional neutrosophic sets and their AHP & TOPSIS extension
Kahraman, C. (2025) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-238761 · Cited by 1
Possibilistic Prediction of Collaborative Filtering Recommender System in the Confidence q-Rung Picture Linguistic Fuzzy Environment
Sirbiladze, G., Kacprzyk, J., Khutsishvili, I., Midodashvili, B., & Gugunava, O. (2024) · The Arabian journal for science and engineering · doi:10.1007/s13369-024-09591-5 · Cited by 1
A novel development to encrypt data communication under t-intuitionistic fuzzy environment
Alolaiyan, H., Latif, L., Shuaib, U., Razaq, A., & Xin, Q. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308140 · Cited by 1
Planning Goal-Directed Activities by an Autonomous Robot Based on Contradictory Information under Conditions of Uncertainty
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α,β,γ− Neutrosophic aggregation operators and their applications in the software site selection
Ali, S., Rahim, M., Bajri, S. A., Ahmad, S., Alharbi, R., & Khalifa, H. A. E.-W. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31417 · Cited by 1
Distribution Linguistic Trust Propagation and Aggregation Based on Numerical Scale and Archimedean t−norm
Zhou, X., Li, S.-L., & Wei, C.-P. (2024) · International Journal of Fuzzy Systems · doi:10.1007/s40815-024-01687-2 · Cited by 1
Investigation of the Usability of the Fuzzy Logic Method in Determining the Strength Results of the Samples Created by Bitlis Region Ignimbrites
Bozkurt, N., & Karaca, E. O. (2023) · Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.47495/okufbed.1103422 · Cited by 1
The Ordered Weighted Average Sector Liquid Return Index: A Method for Determining Financial Recovery from Sectoral Debt
Linares-Mustarós, S., Farreras-Noguer, M. À., Ferrer-Comalat, J. C., & Merigó, J. (2023) · Mathematics · doi:10.3390/math11234839 · Cited by 1
Extend Tversky’s Ratio Model to an Asymmetric Similarity Measurement Model with Three Conditional Parameters: 3p-ASM Model
He, W., Dutta, B., Liu, Y.-Y., & Rodríguez, R. M. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00285-8 · Cited by 1
On Development of Neutrosophic Cubic Graphs with Applications in Decision Sciences
Sultana, F., Gulistan, M., Liu, P.-D., Ali, M., Khan, Z., Al-Shamiri, M., & Azhar, M. (2022) · Journal of Function Spaces · doi:10.1155/2022/8597666 · Cited by 1
“Many other extensions of fuzzy sets have been developed so far like interval-valued fuzzy sets [2] by Zadeh, intuitionistic fuzzy Hindawi Journal of Function Spaces Volume 2022, Article ID 8597666, 24 pages https://doi.”
Method
Retracted
Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System
Sajja, P., & Mishra, R. (2022) · International Journal of System Dynamics Applications · doi:10.4018/ijsda.302633 · Cited by 1
Fermatean fuzzy linguistic weighted averaging/geometric operators based on modified operational laws and their application in multiple attribute group decision-making
Verma, R. (2021) · doi:10.20944/preprints202104.0316.v1 · Cited by 1
A Novel Algorithm to Detect Brain Tumor using Staged-Type-II Fuzzy Classifier
Das, A., & Chatterjee, S. (2020) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci47803.2020.9308426 · Cited by 1
“The objective of the proposed work is to detect the brain tumor and classify the segmented tumor in Fluid-Attenuated Inversion Recovery (FLAIR) weighted MR images into benign or malignant tumor class using Type-II Fuzzy Inference System [1].”
Method
Fuzzy Rule-Based Models
Pedrycz, W. (2020) · An Introduction to Computing with Fuzzy Sets · doi:10.1007/978-3-030-52800-3_13 · Cited by 1
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology: Using Natural Language Processing and Deep Learning for Decision Aid
Zuheros, C., Martínez-Cámara, E., Herrera-Viedma, E., & Herrera, F. (2020) · arXiv.org · doi:10.48550/arxiv.2008.00032 · Cited by 1
“Traditional MpMcDM models allow experts to provide their individual evaluations through numerical ratings [34] or linguistic variables [13, 25, 46].”
Background
2-tuple fuzzy linguistic perceptions and probabilistic awareness-based heuristics for modeling consumer purchase behaviors
Giráldez-Cru, J., Chica, M., Cordón, O., & Herrera, F. (2020) · IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzz48607.2020.9177712 · Cited by 1
The Concept of an it System Supporting the Processes of Operating Technical Facilities in the Military
Przybysz, K., & Grzesik, N. (2020) · Journal of KONBiN · doi:10.2478/jok-2020-0039 · Cited by 1
Challenges of Ethical Evaluation Models for Intelligent Assistive Technologies. An Initial Ethical Model Based on Linguistic Decision Analysis
Espinilla, M., Verdejo, M. A., González, L., Nugent, C., Cruz, A. J., & Medina, J. (2019) · International Conference on Ubiquitous Computing and Ambient Intelligence · doi:10.3390/proceedings2019031022 · Cited by 1
Computational method for monotonic hesitant fuzzy linguistic terms by fitting Weibull distribution function concerns explicit and implicit appraisal information
Liu, Y., Xie, M., Zhu, J.-J., & Hao, J.-J. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-182754 · Cited by 1
Fitting Sugeno integral for learning fuzzy measures using PAVA isotone regression
Beliakov, G., & Divakov, D. (2019) · International Journal of Intelligent Systems · doi:10.1002/int.22172 · Cited by 1
Temporal Relations Between Imprecise Time Intervals: Representation and Reasoning
Ghorbel, F., Hamdi, F., & Métais, E. (2019) · International Conference on Conceptual Structures · doi:10.1007/978-3-030-23182-8_7 · Cited by 1
On Fuzzy Optimization Foundation
Barros, L., Pinto, N. J. B., & Esmi, E. (2019) · Joint IFSA World Congress and NAFIPS Annual Meeting · doi:10.1007/978-3-030-21920-8_14 · Cited by 1
Modeling language constructs with compatibility intervals
Kapustin, P., & Kapustin, M. (2019) · Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures · doi:10.18653/v1/w19-1006 · Cited by 1
Comparing the performance of different types of distributed fuzzy-based traffic signal controllers
Araghi, S., Khosravi, A., & Creighton, D. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-181993 · Cited by 1
Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS
Faraj, B. K., & Hussein, N. K. (2019) · Tikrit Journal of Pure Science · doi:10.25130/j.v24i1.832 · Cited by 1
A multivalued agent-based model for the study of noncommunicable diseases
Aziza, R., Borgi, A., Zgaya, H., & Guinhouya, B. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103101 · Cited by 1
“the LAOs based on the extension principle [42], the LAOs based on symbols [43], and the LAOs based on continuous linguistic domain [39, 44].”
Method
Continuous state and action Q-learning framework applied to quadrotor UAV control
Naruta, A., Mannucci, T., & Kampen, E.-J. V. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-0145 · Cited by 2
Geometric Defuzzification revisited
Greenfield, S. (2018) · Information Sciences · doi:10.1016/j.ins.2018.07.019 · Cited by 1
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Métais, E., Ghorbel, F., Hamdi, F., Ellouze, N., Herradi, N., & Soukane, A. (2018) · Enterp. Model. Inf. Syst. Archit. · doi:10.18417/emisa.si.hcm.11 · Cited by 1
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Applications of computing with words in medicine: Promises and potential
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Application of hybrid uncertainty-clustering approach in pre-processing well-logs
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Evolutionary approach to violating group anonymity using third-party data
Tavrov, D., & Chertov, O. (2016) · SpringerPlus · doi:10.1186/s40064-016-1692-9 · Cited by 1
“Fuzzy sets constitute a core of linguistic variables (Zadeh 1975).”
Background
Existence and uniqueness of two fuzzy solutions to cooperative games
González, E., Espín, R. A., & Mazcorro, G. (2013) · doi:10.2991/.2013.8 · Cited by 1
“…expression is a consequence of Proposition 2: Predicates p(i,C) and a(i,C) correspond to the statements "player i is important to coalition C" and "player i has feasible and advantageous alternatives to reach an agreement within C". p 2 (i,C) models the linguistic modifier related to "very" [11].”
Background
Theoretical methods of constructing fuzzy inference relations
Yuan, X.-H., Li, H.-X., & Wang, X.-N. (2009) · doi:10.1007/s12543-009-0030-0 · Cited by 1
Real-Time Constrained Fuzzy Arithmetic
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The Description of Information in 4-Dimensional Pseudo-Euclidean Information Space
Shro, O. (2007) · arXiv.org · doi:10.48550/arxiv.0709.0993 · Cited by 1
“cles tendered in series ideas fuzzy sets, fuzzy logic [35,36,37] and linguistic variables [38,39].”
BackgroundMethodInfluential
Algorithms for assessing the qualitative and quantitative risks of lending to small and medium-sized businesses based on fuzzy calculus
Sveshnikov, S., Bocharnikov, V., Uvarova, T., & Kovalchuk, P. (2024) · Serbian Journal of Management · doi:10.5937/sjm19-43418 · Cited by 1
Ranking Alternatives by an Extension to Fuzzy VIKOR
Chu, T. (2024) · International Journal of Applied Physics and Mathematics · doi:10.17706/ijapm.2024.14.4.125-134 · Cited by 1
Literature Review of various Fuzzy Rule based Systems
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Computational Intelligence
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“(16.4) This formula represents the extension principle (Zadeh 1975a, 1975b, 1975c).”
Influential
Self evolving Takagi-Sugeno-Kang fuzzy neural network.
Nguyễn, N. N. (2012) · doi:10.32657/10356/50807 · Cited by 1
“NTU-School of Computer Engineering (SCE) 21 defined by Zadeh (1975) [32,43-44] with a quintuple (x, T(x), U, G, M) where x is the name of the variable; T(x) is the linguistic term set of x; U is a universe of discourse; G is a syntactic rule for generating the names of values of x; and M is a semantic rule that associ…”
Background
Covering based approximation - a new type approach
Mohanty, D. (2010) · Int. J. Comput. Vis. Robotics · doi:10.1504/ijcvr.2010.038079 · Cited by 1
Psychological tests from a (fuzzy-)logical point of view
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Cin, S., & Çelebi, U. B. (2026) · Ships and Offshore Structures · doi:10.1080/17445302.2026.2748916
SOFIS: A New Approach to Build Fuzzy Inference Systems
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Chang, A., Ali, Z., Asif, M., Shah, K., & Abdeljawad, T. (2026) · Modeling Earth Systems and Environment · doi:10.1007/s40808-026-02887-5
“…et al. 2024 = (𝒮𝒮 (c) , 𝒮𝒮 (c) , ℳ (c) , ℳ (c)) , the structure of the score degree and accuracy degree is deliberated and designed by: and Definition 3 (Zadeh 1975a, b, c) The mathematical structure of the LTS is deliberated and stated by: { } where Z contained a complete finite ordered…”
Semantic fuzzy bat optimization for regional manufacturing supply chain resilience
Zhao, Z. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02488-2
“Fuzzy logic can effectively handle supply chain uncertainty [19–21], but its combination with metaheuristic algorithms has mostly been conducted in simulations [22, 23], and few engineering validations have been performed under real high-frequency disruption scenarios [24, 25].”
Appraisement of Green Suppliers by Fuzzy and Non-Fuzzy Based Holistic Approach to Sustain Industries: Case Empirical Research
Mannadhan, P. (2026) · Logistics · doi:10.3390/logistics10100231
“According to this method, the importance weights of various levels of criteria and the ratings of numerous alternatives under qualitative criteria are evaluated using linguistic concepts represented by TFs [45].”
A linguistic picture fuzzy number ranking method based on score function and double exact function
Jia, Y.-L., Hu, Y.-Q., Yang, Y., Hu, L.-Y., & Wang, X.-X. (2026) · Journal of Control and Decision · doi:10.1080/23307706.2026.2727471
Consensus reaching process with dynamic consensus threshold model and personalized feedback mechanism for multi-attribute group decision making
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Linguistic time series forecasting model for handling numeric time series’ uncertainty in an axiomatic hedge algebra-based formalism
Phong, P. Đ., Van, L. M., Thông, H. V., Dư, N. Đ., & Hồ, N. C. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x261458481
A novel D-SATMDI control system with tunable inertance for real-time vibration mitigation in offshore platforms
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Spherical fuzzy combined compromise solution approach for decision-making based on similarity and score functions
Bist, B., & Kumar, S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116154
A Multi-Criteria Framework for Software Product Evaluation and Ranking Using Fuzzy d-Sets
Kadam, N., & Mittal, G. (2026) · International Journal of Mathematical Engineering and Management Sciences · doi:10.33889/ijmems.2026.11.4.061

Authors: Zadeh, L. A.
Affiliations: University of California, Berkeley (US)
Corresponding author: Zadeh, L.A.
Venue: Information Sciences
Publisher: Elsevier BV
Published: 1975-01
Volume: 8
Issue: 4
Pages: 301-357
ISSN: 0020-0255 (ISSN-L)
DOI: 10.1016/0020-0255(75)90046-8
OpenAlex: W4240278385
Cited by: 2,555
References: 0
Citations by year: 2026: 42 · 2025: 72 · 2024: 83 · 2023: 106 · 2022: 135 · 2021: 130 · 2020: 134 · 2019: 133 · 2018: 122 · 2017: 100 · 2016: 115 · 2015: 136 · 2014: 95 · 2013: 83 · 2012: 69
FWCI: 13.96
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Information Sciences (publishedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
Social Sciences › Decision Sciences › Management Science and Operations Research › Fuzzy and Soft Set Theory
Physical Sciences › Computer Science › Computational Theory and Mathematics › Rough Sets and Fuzzy Logic
Keywords: ELECTRE II, multi-criteria group decision-making, outranking relations, fuzzy ELECTRE, linguistic variables, approximate reasoning

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The concept of a linguistic variable and its application to approximate reasoning—II

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