Pythagorean fuzzy linguistic decision support model based on consistency-adjustment strategy and consensus reaching process

Liu, Jinpei; Fang, Mengdi; Jin, Feifei; Tao, Zhifu; Du, Pengfei

doi:10.1007/s00500-021-05747-9

Cited by 12 publications

(13 citation statements)

References 37 publications

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“…This is enabled by proposing an aggregation operator and a self-confidence score function to meaningfully adjust the weights of DMs. The authors in [61] introduced the new concept of Pythagorean fuzzy linguistic preference relations (PFLPRs) along with the Pythagorean fuzzy linguistic values (PFLVs) that account for the linguistic membership and non-membership degrees, which are driven from the Pythagorean fuzzy sets theory proposed by Yager et al in 2013 [62]. Based upon the definition of consistency, individual consensus degree (CD), and group CD for PFLPRs, a multi-step feedback mechanism is then proposed to adjust only the individual CD of the worst DM at each iteration.…”

Section: Developed Preference Structuresmentioning

confidence: 99%

“…Representation Structure [61,73] Pythagorean linguistic preference relations [63] Flexible Linguistic Expressions [64] Double hierarchy linguistic preference relations [74] Comparative linguistic expressions [65][66][67] Z-numbers and their extensions [68] Nonlinear preference relations [60] Self-confident linguistic preference relations [70,71] q-rung orthopair fuzzy preference relations [72] Complex intuitionistic fuzzy preference relations [69,75,76] Probabilistic linguistic preference relations [77] Heterogeneous preference relations erence loss is then proposed for FLEs to construct the collective evaluation and the feedback mechanism benefits from consensus rules with minimum preference loss to adapt inconsistent opinions. More recently, a preference structure is proposed in [64] based upon augmenting the concepts of self-confidence degree and double hierarchy linguistic preference relation (DHLPR).…”

Section: Referencementioning

confidence: 99%

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Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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Section: Developed Preference Structuresmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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“…We can find that the aggregated value is also in the form of CPLS and cannot be compared with other values directly [ 40 ], the score value can be further calculated according to equation ( 2 ) mentioned in Section 2.1, which is shown as follows:

…”

Section: The Basic Theoriesmentioning

confidence: 99%

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

Wang

Cao

et al. 2022

Computational Intelligence and Neuroscience

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From trivial matters in life to major scientific projects related to the fate of mankind, decision-making is everywhere. Whether high-quality decisions can be made often directly affects the development of affairs, especially when sudden disasters occur. As the basis of decision-making, data are crucial. The continuously probabilistic linguistic set, a data structure of the fuzzy mathematics, is selected in the paper to collect original data after careful comparisons, because this data structure can fully consider the hesitation of decision-makers and the fuzziness of complex problems. Although all alternatives are costly, the costs of different alternatives still vary greatly; obviously, the low-cost alternative is better than others when the same predetermined goal can be achieved, which is one of the research objectives and characteristics of this paper. Different from other researchers who only take the cost as one of the decision-making indicators, the algorithm proposed in the paper pays much more attention on the cost reduction. When dealing with an emergency, it is often difficult to solve the problem by taking measures only once; usually, multiple rounds of measures are needed. Each round of decision-making has both connections and differences, and the multiround decision-making model is proposed and built in the paper. Different from traditional linear structures, the model mainly adopts the closed-loop structure, which divides the whole process into multiple sub-decision-making points, the severities measured at the current time point will be compared with the values estimated at the latter time point, and then, the differences will be input into the system, the corresponding automatic adjustment modules will be activated immediately according to the values. The accuracy of the system can be verified and adjusted in time by the closed-loop control module. Finally, several experiments are carried out and the results show that the algorithm proposed in the paper is more effective and the cost is lower.

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“…The application of PFS is wider than that of IFSs in expressing the uncertainty in MADM problems. Ever since the first appearance of PFS, there are many studies (Yager 2013 , 2014 ; Zhang and Xu 2014 ; Peng and Yang 2015 , 2016 ; Zhang 2016a , 2016b ; Gou et al 2016 ; Mahanta and Panda 2021 ; Ma et al 2021 ;Du et al 2017 ; Akram et al 2020 ; Liu et al 2021a , 2021b , 2021c , 2021d , 2021e ; Xian and Cheng 2021 ; Zhang and Ma 2020 ; Sarkar and Biswas 2020 ; Shakeel et al 2020 ) on MADM problems under Pythagorean fuzzy circumstances.…”

Section: Introductionmentioning

confidence: 99%

“…The QROFS is usually able to cope with much higher degrees of uncertainty. Since it was initiated, numerous researchers have exploited and utilized it in various areas (Wang et al 2019 ; Du et al 2021 ; Xing et al 2019 ; Ju et al 2019a , 2019b , 2020 ; Gao et al 2019 ; Garg 2021a ; Mahmood and Ali 2021a , 2021b ; Aydemir and Yilmaz Gündüz 2020 ; Rawat and Komal 2022 ; Akram et al 2021 ; Liu et al 2021a , 2021b , 2021c , 2021d , 2021e ).…”

Section: Introductionmentioning

confidence: 99%

Complex q-rung orthopair fuzzy Frank aggregation operators and their application to multi-attribute decision making

et al. 2022

Soft Comput

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The complex q-rung orthopair fuzzy sets (Cq-ROFSs) can serve as a generalization of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets FS (CFSs). Cq-ROFSs provide more freedom for people handling uncertainty and vagueness by the truth and falsity grades on the condition that the sum of the q-powers of the real part and imaginary part is within the unit interval. Further, Frank operational laws are an extended form of Archimedes' T mode and Archimedes' S mode and Frank aggregation operators have a certain parameter which makes them more flexible and more generalized than many other aggregation operators in the process of information fusion. The objectives of this paper are to extend the Frank operations to the complex q-rung orthopair fuzzy environment and to introduce their score function and accuracy function. Meanwhile, some complex q-rung fuzzy Frank aggregation operators are developed, such as the complex q-rung orthopair fuzzy Frank weighted averaging (Cq-ROFFWA) operator, the complex q-rung orthopair fuzzy Frank weighted geometric (Cq-ROFFWG) operator, and the complex q-rung orthopair fuzzy Frank ordered weighted averaging (Cq-ROFFOWA) operator, and their special cases are discussed. In addition, an innovative MADM method is introduced according to the propounded operators to deal with multi-attribute decision-making problems under the complex q-rung orthopair fuzzy environment. Consequently, the practicability and effectiveness of the created methods are proposed by parameter exploration and comparative analysis.

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Pythagorean fuzzy linguistic decision support model based on consistency-adjustment strategy and consensus reaching process

Cited by 12 publications

References 37 publications

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

Complex q-rung orthopair fuzzy Frank aggregation operators and their application to multi-attribute decision making

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