The likelihood-based optimization ordering model for multiple criteria group decision making with Pythagorean fuzzy uncertainty

Chen, Ting‐Yu

doi:10.1007/s00521-020-05278-8

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Zhang et al 38 exploited generalized PF preference functions to promote an advanced preference ranking organization method for enrichment evaluations (PROMETHEE) involving PF information. Leveraging the advantages of PF theory, numerous investigations have focused on the exploitation of PF sets in managing the uncertainties associated with the decision‐maker's judgments and manipulating the MCDA problems under conditions of vagueness and impreciseness 4,6,14,23,24 …”

Section: Preliminariesmentioning

confidence: 99%

“…However, the existing literature may underestimate the contrivance of an appropriate likelihood measure in PF surroundings and its corresponding likelihood‐based MCDA method involving PF uncertainties. At an earlier time, Chen 24 explored an effective likelihood measure to ascertain order relations using PF scalar functions and then structured an optimization ordering model to tackle group decision‐making issues. Fei et al 25 evolved an ordered weighted averaging‐based soft likelihood function to solve MCDA issues within PF environments.…”

Section: Introductionmentioning

confidence: 99%

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Pythagorean fuzzy likelihood function based on beta distributions and its based dominance ordering model in an uncertain multiple criteria decision support framework

Tsao

Chen

2021

Int J Intell Syst

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The concept of Pythagorean fuzzy (PF) sets represents a superior tool to model complex uncertainties in an ambiguous and equivocal decision‐making framework. In consideration of the significant capacity for exhibiting the uncertainty of subjective appraisals and estimations under the aegis of the PF theory, this paper presents a simple‐to‐operate decision‐making approach that is grounded in some beneficial concepts of original likelihood functions and measurements of dominating and dominated characters. On the strength of beta distributions, this paper seeks to propound new notions of PF likelihood functions and likelihood‐oriented dominating/dominated characters and to launch an exploitable multiple criteria evaluation method by means of a dominance ordering model for treating decision analysis within PF environments. This paper initiates an efficient beta distribution‐based approach to the construction of novel PF likelihood functions that can quantify the possibility degrees of outranking and outranked relationships between Pythagorean membership grades. The applicable satisfaction and dissatisfaction estimations are established on the likelihood‐oriented dominating and dominated characters, respectively. Furthermore, this paper formulates a straightforward dominance ordering model to obtain the ultimate dominance ranking orders of candidate alternatives and accomplish multiple criteria decision‐making issues involving complicated uncertainty. A financing decision‐making problem concerning working capital requirements is investigated to validate the application results using the advanced methodology. The real‐world application is implemented to examine the reasonableness and efficacy of the established techniques. Moreover, comparative studies through the utility of a sensitivity analysis are performed to demonstrate the efficacy and merits of the dominance ordering model. The comparison results manifest that the initiated methodology is an advantageous and reliable decision‐making technique that can enhance the methodological development regarding the multiple criteria evaluation model under PF uncertainty. Finally, recommendations for future research directions are also presented in the conclusions.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pythagorean fuzzy likelihood function based on beta distributions and its based dominance ordering model in an uncertain multiple criteria decision support framework

Tsao

Chen

2021

Int J Intell Syst

Self Cite

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“…After their successful utilization, certain scholars have employed it in the natural environment of separated areas. For example, Garg [19] explored interval-valued PFS and their applications, Ayyildiz and Gumus [20] utilized the AHP method based on intervalvalued PFS, Ejegwa et al [21] implemented the correlation measures by using the PFS, Zhao et al [22] explored TODIM method for interval-valued PFS, Gao et al [23] developed the quantum Pythagorean fuzzy evidence theory, Pan et al [24] proposed similarity measures for PFS, Zulqarnain et al [25] initiated the TOPSIS method for Pythagorean fuzzy hyper-soft sets, Rani et al [26] developed the weighted discrimination based approximation approach by using the PFS, Calik [27] initiated the AHP and TOPSIS method for PFS and discussed their application in green supplier chain management, Chen [28] developed the likelihood-based optimization based on PFS. The proposition of neutrosophic soft expert set was put forward by Broumi et al [60] which allowed the explanation of images and inverse images of neutrosophic soft expert frames.…”

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confidence: 99%

The Prognosis of Allergy-Based Diseases Using Pythagorean Fuzzy Hypersoft Mapping Structures and Recommending Medication

et al. 2022

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The Pythagorean fuzzy set (PFS), which Yager extended, is a novel tool for dealing with ambiguity when considering membership(MS) grade p and non-membership (NMS) q that fulfil the criteria p 2 +q 2 ≤ 1 allowing the structure to characterise fuzziness more thoroughly and precisely than intuitionistic fuzzy sets. The Pythagorean Fuzzy Hypersoft (PFHS) set theory is a useful method for dealing with abnormalities and uncomfortable information in real-life situations. The Pythagorean fuzzy set and the Hypersoft set, which comprise the truthfulness grade (TG) and falsehood grade (FG) in the Pythagorean environment, are combined in the PFHS principle. This study tries to define the arguments around allergy diagnosis and the effects that arise with it. After considering the dire outcomes of Allergies, it becomes challenging to distinguish between the various types of Allergies and their complexity. Because the false portions of practical assessments are usually ignored, precision in the person's developmental history is difficult to recognize, and number of sessions cannot be projected. This work presents the PFHS set and PFHS mapping with its inverse mapping (INM) to alleviate these constraints. These notions are capable and necessary for correctly analyzing the problem by combining it with scientific modelling. This study establishes a link between symptoms and medications, lowering the narrative's complexity. For the various types of allergies, a table based on a fuzzy interval between [0, 1] is created. The techniques that are based on PFHS-mapping, which is used to effectively identify a problem then choose the right treatment for each patient's condition.

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A parametric likelihood measure with beta distributions for Pythagorean fuzzy decision-making

Tsao

Chen

2022

Neural Comput & Applic

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The likelihood-based optimization ordering model for multiple criteria group decision making with Pythagorean fuzzy uncertainty

Cited by 5 publications

References 39 publications

Pythagorean fuzzy likelihood function based on beta distributions and its based dominance ordering model in an uncertain multiple criteria decision support framework

Pythagorean fuzzy likelihood function based on beta distributions and its based dominance ordering model in an uncertain multiple criteria decision support framework

The Prognosis of Allergy-Based Diseases Using Pythagorean Fuzzy Hypersoft Mapping Structures and Recommending Medication

A parametric likelihood measure with beta distributions for Pythagorean fuzzy decision-making

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