المؤلفون: Rani, Pratibha, Mishra, Arunodaya R., Saha, Abhijit, Hezam, Ibrahim M., Pamucar, Dragan

المصدر: International Journal of Intelligent Systems; Mar2022, Vol. 37 Issue 3, p2612-2647, 36p

مصطلحات موضوعية: WASTE treatment, FOOD waste, DECISION making, FUZZY numbers, FUZZY measure theory, FUZZY sets

مستخلص: Uncertainty is often occurred in real‐life decision‐making problems due to the lack of complete information, imprecise data, and the vagueness of decision making experts in qualitative judgment, thus, the crisp values of criteria may be insufficient to handle such types of complex real situations. As the extension of fuzzy set, intuitionistic fuzzy set and Pythagorean fuzzy set, the Fermatean Fuzzy Set (FFS) has been demonstrated as a powerful tool to handle the uncertainty arisen in practical decision‐making problems. Thus, this study aims to introduce an integrated Fermatean fuzzy information‐based decision‐making method by combining method based on the removal effects of criteria (MEREC) and additive ratio assessment (ARAS) methods with the application in a food waste treatment technology selection problem. By using Fermatean fuzzy numbers, the suggested approach successfully handle the qualitative data and uncertain information that often occur in practical situations. This study consists of four phases. First, entropy measure is developed for FFS and further utilized for determining the experts' weights. Second, some Fermatean fuzzy Heronian mean operators and their properties are introduced to aggregate the Fermatean fuzzy information. These operators can provide us a valuable means to handle practical multicriteria decision‐making problems on FFSs context. Third, an extended MEREC technique is originated to assess objective criteria weights within FFS context. Fourth, an integrated ARAS method is introduced with the combination of proposed entropy measure, generalized weighted Fermatean fuzzy Heronian mean operator and MEREC technique to evaluate and rank the alternatives. To confirm the reasonableness and practicality of the proposed methodology, an empirical case study of food waste treatment technology selection is discussed on FFSs settings. Further, a comparison with extant models and a sensitivity investigation are performed to confirm the validity and robustness of the obtained outcomes. [ABSTRACT FROM AUTHOR]

: Copyright of International Journal of Intelligent Systems is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Arya, Vikas, Kumar, Satish

المصدر: International Journal of Intelligent Systems; Nov2021, Vol. 36 Issue 11, p6837-6870, 34p

مصطلحات موضوعية: FUZZY measure theory, MEDICAL supplies, FUZZY sets, INFORMATION measurement, GROUP decision making, SUPPLY chain management, FUZZY numbers

مستخلص: Nowadays, supply chain management (SCM) has achieved considerable attention from all over the world. q‐rung orthopair fuzzy set, developed by Yager, is the entirety of the most prominent tool to express fuzzy data in the decision‐making problems. In this study, the introduction of two new generalised measures (entropy and Jensen–Tsalli divergence measure) of q‐rung orthopair fuzzy information involving one real parameter is given. The proposed measures have satisfied all the necessary mathematical properties of being a measure. Then the introduced entropy and divergence measure is used to obtain the objective weights. Based on the proposed entropy and divergence measure, we proposed a new decision method to deal with multiple‐attribute group decision‐making problems under the q‐rung orthopair fuzzy environment. Then, on the basis of the TODIM and VIKOR techniques, an integrated TODIM‐VIKOR approach is developed to solve multiattribute group decision‐making problem. In this paper, TODIM aims to determine the overall dominance degree and VIKOR aims to determine the compromise solution. Lastly, we handle a supplier selection problem to verify the performance of the proposed q‐rung orthopair fuzzy TODIM‐VIKOR method and results explore the reliability and effectiveness of our proposed methodology by comparing the ranking solution with the ranking results of the existing approaches. [ABSTRACT FROM AUTHOR]

: Copyright of International Journal of Intelligent Systems is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Deng, Zhan, Wang, Jianyu

المصدر: International Journal of Intelligent Systems; Oct2021, Vol. 36 Issue 10, p5866-5886, 21p

مصطلحات موضوعية: DECISION making, DEMPSTER-Shafer theory, FUZZY sets, FUZZY numbers, FUZZY measure theory, INFORMATION processing, AXIOMS

مستخلص: Fermatean fuzzy set (FFS) is an effective tool to depict expert reasoning information in the decision‐making process. In this study, we first propose a novel Fermatean fuzzy entropy measure to describe the fuzziness degree of FFSs. The new Fermatean fuzzy entropy takes into account the uncertainty information and the indeterminacy degree of FFSs. Subsequently, we prove that Fermatean fuzzy entropy satisfies the axiom requirement of fuzzy entropy measure. Thereafter, a novel Fermatean fuzzy multicriteria decision‐making approach is developed based on Dempster–Shafer theory with the help of the Fermatean fuzzy entropy. The proposed method modeled each Fermatean fuzzy number as a piece of evidence, and the weights of criteria are determined by the entropy measure of FFSs. Then, the weighted average evidence for the alternatives under all criteria is computed from the weights of criteria. Later, Dempster's combination rule is leveraged to combine the weighted average evidence of the alternatives to obtain the final evaluation information about each alternative. The proposed approach can effectively deal with the uncertain information in decision‐making problems and help reduce the information loss in the decision‐making process. Ultimately, the feasibility and validity of the proposed approach are demonstrated through two practical instances. [ABSTRACT FROM AUTHOR]

: Copyright of International Journal of Intelligent Systems is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Chao, Kun, Zhao, Hua, Xu, Zeshui

المصدر: International Journal of Intelligent Systems; Sep2021, Vol. 36 Issue 9, p5264-5306, 43p

مصطلحات موضوعية: FUZZY measure theory, FUZZY numbers, FUZZY sets, CLUSTER analysis (Statistics), NUMERICAL analysis, DISTANCES, DECISION making

مستخلص: Distance measure is an essential tool to characterize the difference between two samples. Recently, lots of distance measures have been proposed for hesitant fuzzy sets (HFSs). In this paper, we shall propose some novel distance formulas to measure the deviation between two HFSs. First, we define some new concepts including the hesitant fuzzy variance, covariance, and correlation coefficient. Based on these concepts and the idea of the traditional Mahalanobis distance, the hesitant Mahalanobis distance between two HFSs is developed. Then we discuss the properties of the new distance measure and uncover the significant characteristic of the introduced distance measure that it can give the attributes an adaptive weight and can eliminate the influence of the correlation between the attributes under hesitant fuzzy environment. And then, some extensions of this new distance measure are also developed. Second, to show the validity and applicability of the proposed distance measures, we compare them with the existing ones in decision making and cluster analysis with some numerical examples. Third, using the proposed distance measures, we develop two algorithms to estimate the optimal number of clusters, which is a new application area of the hesitant fuzzy distance measures. Finally, the two algorithms are applied in a numerical example to illustrate their applicability and efficiency. [ABSTRACT FROM AUTHOR]

: Copyright of International Journal of Intelligent Systems is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Maturo, Antonio

المصدر: International Journal of Intelligent Systems; Dec2011, Vol. 26 Issue 12, p1196-1205, 10p

مصطلحات موضوعية: FUZZY measure theory, STATISTICAL decision making, PROBABILITY theory, NUMERICAL analysis, FUZZY numbers, EXTENSION (Logic), NON-Euclidean geometry, GENERALIZATION

مستخلص: In assigning weights and scores in a decision problem usually we assume that they are finitely additive normalized measures, i.e., from the formal point of view, finitely additive probabilities. The normalization requirement sometimes appears as an actual restriction. For instance, it happens when the weights and scores can not be assigned in precise numerical terms or some logical and numerical issues arising from the given problem imply conditions that are different from the property of additivity. We must then consider extensions of the concept of probability. One, introduced by Zadeh, is to express the probabilities with fuzzy numbers; another extension, considered by Sugeno, Weber, and others, is first to replace the additivity with the condition of monotonicity, much weaker, and then to identify conditions 'intermediate' between monotonicity and additivity. In any case, by assigning weights and scores, they must be consistent with the point of view considered. The conditions of consistency of finitely additive probabilities and their generalizations were discussed in several papers. This paper proposes an extension of the concept of finitely additive probability from a purely geometric point of view. Specifically, the environment of Euclidean Geometry, as de Finetti used to define the consistency of an assignment of probabilities, is replaced by the more general environment of Join Geometry by Prenowitz and Jantosciak. In this context, we introduce the concept of coherent join measure, i.e., normalized measure that is consistent with a join system, in particular, a join space or a join geometry. We show that decomposable measures with respect to a t-conorm are special cases of join coherent measures. Finally, we present some applications, significant special cases, and possible lines of research. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

: Copyright of International Journal of Intelligent Systems is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)