Pub. online:4 Aug 2021Type:Research ArticleOpen Access
Journal:Informatica
Volume 32, Issue 4 (2021), pp. 759–794
Abstract
From the perspective of multiple attribute decision analysis, the evaluation of decision alternatives should be based on the performance scores determined with respect to more than one attribute. Fuzzy logic concepts can equip the evaluation process with different scales of linguistic terms to let the decision-makers point out their ideas and preferences. A more recent one of fuzzy sets is the picture fuzzy set which covers three separately allocable elements: positive, neutral, and negative membership degrees. The novel and distinctive element included by a picture fuzzy set is the refusal degree which is equal to the difference between 1 and the sum of the other three. In this study, we aim to contribute to the literature of the picture fuzzy sets by (i) proposing two novel entropy measures that can be used in objective attribute weighting and (ii) developing a novel picture fuzzy version of CODAS (COmbinative Distance-based ASsessment) method which is empowered with entropy-based attribute weighting. The applicability of the method is shown in a green supplier selection problem. To clarify the differences of the proposed method, a comparative analysis is provided by considering traditional CODAS, spherical fuzzy CODAS, and spherical fuzzy TOPSIS with different entropy-based scenarios.
Pub. online:1 Jan 2018Type:Research ArticleOpen Access
Journal:Informatica
Volume 29, Issue 3 (2018), pp. 517–537
Abstract
Quantitative and qualitative fuzzy information measures have been proposed to solve multi-attribute decision making (MADM) problems with interval–valued hesitant fuzzy information from different points. We analyse the existing fuzzy information measures of the interval-valued hesitant fuzzy sets (IVHFSs) in detail and classify them into two categories. One is based on the closeness of the data, such as the distance, and the other is based on the linear relationship or variation tendency, such as the correlation coefficient. These two kinds of information measures are actually partial measures which pay attention to only one factor of the data. Therefore, we construct a novel synthetic grey relational degree by considering both the closeness and the variation tendency factors of the data to improve the existing information measures and enhance the grey relational analysis (GRA) theory for IVHFSs. However, the notion of the synthetic grey relational degree is not only restricted to the IVHFSs but can be extended to other sets. Furthermore, we employ two practical MADM examples about emergency management evaluation and pattern recognition to validate and compare the proposed synthetic grey relational degree with other information measures, which demonstrate its superiorities in discrimination and accuracy.