Hesitant 2-tuple linguistic Bonferroni operators and their utilization in group decision making

Wang, Lidong; Wang, Yanjun; Pedrycz, Witold

doi:10.1016/j.asoc.2019.01.038

Cited by 32 publications

(19 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some intuitionistic fuzzy interaction partitioned BM operators have been proposed by Liu et al [37]. The BM has been extended to accommodate interval type-2 fuzzy values [38], Pythagorean fuzzy values [39]- [41], 2-tuple intuitionistic fuzzy values [42], hesitant 2-tuple linguistic argument [43], neutrosophic fuzzy values [44], etc. Dombi [45] developed Dombi t-norm and Dombi t-conorm, which is more flexible by a parameter in aggregation process.…”

Section: Some Q-rung Orthopair Fuzzy Bonferroni Mean Operatorsmentioning

confidence: 99%

New q-Rung Orthopair Fuzzy Bonferroni Mean Dombi Operators and Their Application in Multiple Attribute Decision Making

Yang

Pang

2020

IEEE Access

View full text Add to dashboard Cite

Some q-rung orthopair fuzzy Bonferroni mean Dombi aggregation operators have been developed based on the Bonferroni mean, Dombi T-norm and T-conorm in q-rung orthopair fuzzy environment. The q-rung orthopair fuzzy Bonferroni mean Dombi averaging (q-ROFBMDA) operator and the q-rung orthopair fuzzy geometric Bonferroni mean Dombi averaging (q-ROFGBMDA) operator are first developed. Then the q-rung orthopair fuzzy weighted Bonferroni mean Dombi averaging (q-ROFWBMDA) operator and the q-rung orthopair fuzzy weighted geometric Bonferroni mean Dombi averaging (q-ROFWGBMDA) operator have been developed. Based on the partitioned operation, the q-rung orthopair fuzzy partitioned Bonferroni mean Dombi averaging (q-ROFPBMDA) operator and the q-rung orthopair fuzzy partitioned geometric Bonferroni mean Dombi averaging (q-ROFPGBMDA) operator have been presented. Some desirable properties of the new aggregation operators have been studied. A new multiple attribute decision making method based on the q-ROFWBMDA (q-ROFWGBMDA) operator is proposed. Finally, a numerical example of new campus site selection has been presented to illustrate the new method. INDEX TERMS q-rung orthopair fuzzy sets, Dombi, Bonferroni mean, aggregation operator.

show abstract

Section: Some Q-rung Orthopair Fuzzy Bonferroni Mean Operatorsmentioning

confidence: 99%

New q-Rung Orthopair Fuzzy Bonferroni Mean Dombi Operators and Their Application in Multiple Attribute Decision Making

Yang

Pang

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In this model, a linguistic fuzzy variable is regarded as a continuous variable within its definitional domain and a dual combination formed by a linguistic fuzzy variable, and a real number is used to express the linguistic assessment information (Rao et al 2017a). Subsequently, some extended models were proposed, i.e., 2-tuple hybrid ordered weighted averaging (THOWA) operator (Rao et al 2015), interval 2-tuple linguistic VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian (ITL-VIKOR) method (You et al 2015), gray linguistic 2-tuple weighted averaging (GLTWA) operator (Rao et al 2016b), 2-tuple linguistic Data Envelopment Analysis (DEA) (Geng et al 2017), linguistic 2-tuple gray correlation degree model (Rao et al 2017a), 2-tuple linguistic generalized aggregation (I2LGA) operator (Liu and Chen 2018), Hesitant 2-tuple linguistic Bonferroni operators (Wang et al 2019), 2-tuple fuzzy linguistic approach (Muhuri and Gupta 2020), two-tuple mixed correlation degree (Rao et al 2020a), multi-source heterogeneous multi-attribute decision-making (MSHMADM) method based on the linguistic 2-tuple (Xiao et al 2020c, d). These studies show that the data process in the two-tuple model and its extended models can effectively avoid the information loss and information distortion in the process of information gathering comparing with some existing congeneric methods.…”

Section: Research Status On Influencing Factors Of Population Agingmentioning

confidence: 99%

Influencing factors analysis and development trend prediction of population aging in Wuhan based on TTCCA and MLRA-ARIMA

Rao

Gao

2021

Soft Comput

View full text Add to dashboard Cite

With the rapid development of the economy, the problem of population aging has become increasingly prominent. To analyse the key factors affecting population aging effectively and predict the development trend of population aging timely are of great significance for formulating relevant policies scientifically and reasonably, which can mitigate the effects of population aging on society. This paper analyses the current situation of population aging in Wuhan of China and discusses the main factors affecting the population aging quantitatively, and then establishes a combination prediction model to forecast the population aging trend. Firstly, considering the attribute values of the primary influence factors are multisource heterogeneous data (the real numbers, interval numbers and fuzzy linguistic variables coexist), a two-tuple correlation coefficient analysis method is proposed to rank the importance of the influencing factors and to select the main influencing factors. Secondly, a combination prediction model named Multiple Linear Regression Analysis-Autoregressive Integrated Moving Average is established to predict the number and the proportion of aging population in Wuhan. By using the statistical data of Wuhan in the past 20 years, this combination prediction model is used for empirical analysis, and a prediction result of the number and the proportion of aging people in Wuhan in the future is obtained. Based on these quantitative analysis results, we propose some countermeasures and suggestions on how to alleviate the population aging of Wuhan from aspects of economic development, pension security system design and policy formulation, which provide theoretical basis and method reference for relevant population management departments to make scientific decisions.

show abstract

“…According to [39][40][41], the greater the hesitant degree of evaluation information, the lower the reliability could be. Thus, the expert with higher hesitant degree should be endowed with a lower weight.…”

Section: Determine Expert Weights Based On a Combination Weighting Mementioning

confidence: 99%

Identification of Key Performance Indicators for Hospital Management Using an Extended Hesitant Linguistic DEMATEL Approach

et al. 2019

View full text Add to dashboard Cite

Performance analysis is of great significance to increase the operational efficiency of healthcare organizations. Healthcare performance is influenced by numerous indicators, but it is unrealistic for administrators to improve all of them due to the restriction of resources. To solve this problem, we integrated double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) with the decision-making trial and evaluation laboratory (DEMATEL) and proposed a DHHFL– DEMATEL method to identify key performance indicators (KPIs) in healthcare management. For the developed approach, the judgments of experts on the inter-relationships among indicators were represented by DHHFLTSs, and a novel combination weighting approach was proposed to obtain experts’ weights in line with hesitant degree and consensus degree. Then, the normal DEMATEL method was extended and used for examining the cause and effect relationships between indicators; the technique for the order of preference by similarity to the ideal solution (TOPSIS) method was utilized to generate the ranking of performance indicators. Finally, the feasibility and effectiveness of the proposed DHHFL–DEMATEL approach were illustrated by a practical example in a rehabilitation hospital.

show abstract

Hesitant 2-tuple linguistic Bonferroni operators and their utilization in group decision making

Cited by 32 publications

References 54 publications

New q-Rung Orthopair Fuzzy Bonferroni Mean Dombi Operators and Their Application in Multiple Attribute Decision Making

New q-Rung Orthopair Fuzzy Bonferroni Mean Dombi Operators and Their Application in Multiple Attribute Decision Making

Influencing factors analysis and development trend prediction of population aging in Wuhan based on TTCCA and MLRA-ARIMA

Identification of Key Performance Indicators for Hospital Management Using an Extended Hesitant Linguistic DEMATEL Approach

Contact Info

Product

Resources

About