114 research outputs found
BanzhafâChoquet-copula-based aggregation operators for managing q-rung orthopair fuzzy information
Multi-perspective evaluation of integrated active cooling systems using fuzzy decision making model
As global median temperatures continue to rise, the demand for active cooling systems (ACs) is increasing. These systems are particularly prevalent in developed countries for maintaining comfort during hot weather. Various ACs technologies are available, and assessing their performance in multi-perspective settings is necessary to determine the best option for intended usage. This requires an evaluation platform for assessment. This paper presents a novel multi-criteria decision-making (MCDM) model based on a new integrated 2-tuple linguistic Pythagorean fuzzy-weighted zero-inconsistency (2 TLP-FWZIC) and modified 2-tuple linguistic Pythagorean fuzzy multi-attributive border approximation area comparison (2TLPF-MABAC). The former is used to determine the importance of assessment criteria, while the latter is employed for selecting the optimal ACs using the obtained weights. The first-level weighting results reveal that performance criteria were predominantly favored for assessment, with âenergy performanceâ acquiring the most significant weight (0.2487) among all performance criteria. In terms of ACs selection results, among the 20 tested and assessed systems, the âgeothermal borehole electricity-based ACsâ obtained the highest score value (0.1296), while the âwindow packaged electricity-based ACsâ had the lowest score (-0.0515). The robustness of the results was confirmed through sensitivity analysis
Q-rung orthopair normal fuzzy aggregation operators and their application in multi-attribute decision-making
© 2019 by the authors. Q-rung orthopair fuzzy set (q-ROFS) is a powerful tool to describe uncertain information in the process of subjective decision-making, but not express vast objective phenomenons that obey normal distribution. For this situation, by combining the q-ROFS with the normal fuzzy number, we proposed a new concept of q-rung orthopair normal fuzzy (q-RONF) set. Firstly, we defined the conception, the operational laws, score function, and accuracy function of q-RONF set. Secondly, we presented some new aggregation operators to aggregate the q-RONF information, including the q-RONF weighted operators, the q-RONF ordered weighted operators, the q-RONF hybrid operator, and the generalized form of these operators. Furthermore, we discussed some desirable properties of the above operators, such as monotonicity, commutativity, and idempotency. Meanwhile, we applied the proposed operators to the multi-attribute decision-making (MADM) problem and established a novel MADM method. Finally, the proposed MADM method was applied in a numerical example on enterprise partner selection, the numerical result showed the proposed method can effectively handle the objective phenomena with obeying normal distribution and complicated fuzzy information, and has high practicality. The results of comparative and sensitive analysis indicated that our proposed method based on q-RONF aggregation operators over existing methods have stronger information aggregation ability, and are more suitable and flexible for MADM problems
Novel possibility Pythagorean interval valued fuzzy soft set method for a decision making
We discuss the theory of possibility Pythagorean interval valued fuzzy soft set, possibility interval valued fuzzy soft set and define some related the operations namely complement, union, intersection, AND and OR. The possibility Pythagorean interval valued fuzzy soft sets are a generalization of soft sets. Notably, we showed DeMorganâs laws that are valid in possibility Pythagorean interval valued fuzzy soft set theory. Also, we propose an algorithm to solve the decision making problem based on soft set method. To compare two possibilities Pythagorean interval valued fuzzy soft sets for dealing with decision making problems and find a similarity measure is obtained. Finally, an illustrative example is discussed to prove that they can be effectively used to solve problems with uncertainties.Publisher's Versio
Determine OWA operator weights using kernel density estimation
Some subjective methods should divide input values into local
clusters before determining the ordered weighted averaging
(OWA) operator weights based on the data distribution characteristics
of input values. However, the process of clustering input values
is complex. In this paper, a novel probability density based
OWA (PDOWA) operator is put forward based on the data distribution
characteristics of input values. To capture the local cluster
structures of input values, the kernel density estimation (KDE) is
used to estimate the probability density function (PDF), which fits
to the input values. The derived PDF contains the density information
of input values, which reflects the importance of input
values. Therefore, the input values with high probability densities
(PDs) should be assigned with large weights, while the ones with
low PDs should be assigned with small weights. Afterwards, the
desirable properties of the proposed PDOWA operator are investigated.
Finally, the proposed PDOWA operator is applied to handle
the multicriteria decision making problem concerning the evaluation
of smart phones and it is compared with some existing
OWA operators. The comparative analysis shows that the proposed
PDOWA operator is simpler and more efficient than the
existing OWA operator
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