Begell House Inc.
International Journal for Uncertainty Quantification
IJUQ
2152-5080
7
6
2017
HESITANT TRAPEZOIDAL FUZZY AGGREGATION OPERATORS BASED ON ARCHIMEDEAN t-NORM AND t-CONORM AND THEIR APPLICATION IN MADM WITH COMPLETELY UNKNOWN WEIGHT INFORMATION
475-510
10.1615/Int.J.UncertaintyQuantification.2017020585
Xindong
Peng
School of Information Sciences and Engineering, Shaoguan University, Shaoguan, 521005,
China
multiple attribute decision-making
hesitant trapezoidal fuzzy elements
objective weight
hesitant trapezoidal fuzzy aggregation operators
In this paper, we investigate the multiple attribute decision-making (MADM) problems in which the attribute values take the form of hesitant trapezoidal fuzzy elements (HTFEs). The existing t-norms and t-conorms, including the
algebraic, Einstein, Frank, and Hamacher t-norms and t-conorms, can be regarded as special cases of Archimedean
t-norm and t-conorm. Firstly, we develop some new operational laws for HTFEs based on the Archimedean t-norm
and t-conorm. Then, based on the operational laws, we define some hesitant trapezoidal fuzzy aggregation operators
and their generalizations are also introduced, and some desirable properties and the relationships of these operators are discussed in detail. Meanwhile, we present two objective weight determination methods. Inspired by the idea of dependent aggregation, we propose some dependent hesitant trapezoidal fuzzy aggregation operators: the dependent hesitant trapezoidal fuzzy Frank ordered weighted average (DHTFFOWA) operator and the dependent hesitant trapezoidal
fuzzy Frank ordered weighted geometric (DHTFFOWG) operator. Furthermore, we develop an approach to MADM under hesitant trapezoidal fuzzy environment. Finally, an illustrative example for software development project selection is given to verify the developed method and to demonstrate its applicability and validity.
VARIANCE-BASED SENSITIVITY INDICES OF COMPUTER MODELS WITH DEPENDENT INPUTS: THE FOURIER AMPLITUDE SENSITIVITY TEST
511-523
10.1615/Int.J.UncertaintyQuantification.2017020291
S.
Tarantola
Directorate for Energy, Transport and Climate, Joint Research Centre, European Commission,
Ispra (VA), Italy, 21027
Thierry A.
Mara
PIMENT, EA 4518, Université de La Réunion, FST, 15 Avenue René Cassin, 97715 Saint-Denis, Réunion; Directorate for Modelling, Indicators and Impact Evaluation, Joint Research Centre, European Commission, Ispra (VA), Italy, 21027
Fourier amplitude sensitivity test
inverse Rosenblatt transformation
inverse Nataf transformation
variance-based sensitivity indices
dependent contributions
independent contributions
Several methods are proposed in the literature to perform global sensitivity analysis of computer models with independent inputs. Only a few allow for treating the case of dependent inputs. In the present work, we investigate how
to compute variance-based sensitivity indices with the Fourier amplitude sensitivity test. This can be achieved with the help of the inverse Rosenblatt transformation or the inverse Nataf transformation. We illustrate this on two distinct benchmarks. As compared to the recent Monte Carlo based approaches recently proposed by the same authors
[Mara, T.A., Tarantola, S., and Annoni, P., Non-parametric methods for global sensitivity analysis of model output with dependent inputs, Env. Model. Software, 72:173–183, 2015], the new approaches allow us to divide the computational effort by 2 to assess the entire set of first-order and total-order variance-based sensitivity indices.
SOME INTERVAL NEUTROSOPHIC HESITANT UNCERTAIN LINGUISTIC BONFERRONI MEAN AGGREGATION OPERATORS FOR MULTIPLE ATTRIBUTE DECISION-MAKING
525-572
10.1615/Int.J.UncertaintyQuantification.2017020094
Peide
Liu
School of Management Science and Engineering, Shandong University of Finance and
Economics, Jinan Shandong 250014, China; School of Economics and Management, Civil Aviation University of China, Tianjin 300300,
China
Fei
Teng
School of Management Science and Engineering, Shandong University of Finance and
Economics, Jinan Shandong 250014, China
multiple attribute decision-making
interval neutrosophic hesitant uncertain linguistic set
Bonferroni mean
interval neutrosophic hesitant uncertain linguistic Bonferroni mean aggregation operators
Interval neutrosophic hesitant uncertain linguistic set (INHULS) has the advantages of both interval neutrosophic hesitant numbers and uncertain linguistic variables. In this paper, we firstly introduce the definition, the operational laws, and the score function of INHULS. Then, we combine the interval neutrosophic hesitant uncertain linguistic set with
the Bonferroni mean operator and propose some new aggregation operators, such as the interval neutrosophic hesitant uncertain linguistic Bonferroni mean (INHULBM) operator, the interval neutrosophic hesitant uncertain linguistic weighted Bonferroni mean (INHULWBM) operator, the interval neutrosophic hesitant uncertain linguistic geometric Bonferroni mean (INHULGBM) operator, and the interval neutrosophic hesitant uncertain linguistic weighted
geometric Bonferroni mean (INHULWGBM) operator. At the same time, the related properties of these operators are
discussed. Furthermore, we propose two multiple attribute decision-making methods based on the INHULWBM operator
and the INHULWGBM operator. Finally, we give an illustrative example to demonstrate the practicality and effectiveness of the proposed methods.
INDEX, VOLUME 7, 2017
573-578
10.1615/Int.J.UncertaintyQuantification.v7.i6.40