Journals Information
Mathematics and Statistics Vol. 9(5), pp. 669 - 684
DOI: 10.13189/ms.2021.090507
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Hesitant Fuzzy Network Approach for Alternatives Selection with Incomplete Weight Information
Shahira Shafie 1,*, Abdul Malek Yaakob 1,2
1 School of Quantitative Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia
2 Institute of Strategic Industrial Decision Modeling, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
ABSTRACT
Networked rule bases in fuzzy system, acknowledged as fuzzy network, carries multiple stages of development in decision making processes that involves the uncertainty in the data used as medium in various field. Fuzzy network promotes transparency in multicriteria decision making (MCDM) whereby the criteria are divided into subsystems of cost and benefit to ensure good assessment performance. By considering Hesitant fuzzy sets (HFS), which gives the permission of a set of possible values to present the membership degree of an element, we develop a novel approach that applies fuzzy network and the maximizing deviation method in solving MCDM problem. Fuzzy network addresses transparency in the formulation and maximizing deviation method can restore weight information in MCDM problems whether partially known or fully unknown. The proposed method is applied in case study of stock evaluation that carries opinion evaluated by several decision makers and compared in terms of performance using Spearman rho correlation.
KEYWORDS
Merging Operations, MCDM, Maximizing Deviation, Stock Selection, Transparency, Vertical Merging, Maximizing Deviation Method
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Shahira Shafie , Abdul Malek Yaakob , "Hesitant Fuzzy Network Approach for Alternatives Selection with Incomplete Weight Information," Mathematics and Statistics, Vol. 9, No. 5, pp. 669 - 684, 2021. DOI: 10.13189/ms.2021.090507.
(b). APA Format:
Shahira Shafie , Abdul Malek Yaakob (2021). Hesitant Fuzzy Network Approach for Alternatives Selection with Incomplete Weight Information. Mathematics and Statistics, 9(5), 669 - 684. DOI: 10.13189/ms.2021.090507.