Frank Aggregation Operators and Their Application to Probabilistic Hesitant Fuzzy Multiple Attribute Decision-Making
- PDF / 601,912 Bytes
- 22 Pages / 595.276 x 790.866 pts Page_size
- 40 Downloads / 200 Views
Frank Aggregation Operators and Their Application to Probabilistic Hesitant Fuzzy Multiple Attribute Decision-Making Muhammad Yahya1 • Saleem Abdullah1 • Ronnason Chinram2,3 Yasser D. Al-Otaibi4 • Muhammad Naeem5
•
Received: 30 January 2020 / Revised: 19 September 2020 / Accepted: 23 September 2020 Ó Taiwan Fuzzy Systems Association 2020
Abstract The fuzzy aggregation information plays an important role in the group decision support system under the interval-valued hesitant fuzzy information and intervalvalued probabilistic hesitant fuzzy information. Therefore, in this paper, we develop a new approach of the intervalvalued hesitant fuzzy Frank aggregation (IVHFFA) and interval-valued probabilistic hesitant fuzzy aggregation (IVPHFFA) operators. First, we define some operational laws of IVHEs and IVPHFEs by using Frank t-norm and t-conorm. Furthermore, we develop a series of IVHFFA and IVPHFFA operators based on these operational laws
under the IVHF and IVPHF information. Also, discuss some fundamental properties and relations of the proposed aggregation operators for IVHF and IVPHF information. In order to implement the proposed aggregation operators of IVHF and IVPHF information in group decision-making problem, we construct general algorithms for multi-attribute group decision-making problem based on the proposed IVHFFA and IVPHFFA. Finally, from the comparative and sensitivity analysis, the proposed fuzzy decision-making model is more effective and reliable as compared with existing method.
& Ronnason Chinram [email protected]
Keywords Hesitant fuzzy set Probabilistic hesitant fuzzy aggregation operator Decision-making problem Fuzzy decision support system
Muhammad Yahya [email protected] Saleem Abdullah [email protected]
1 Introduction
Yasser D. Al-Otaibi [email protected]
The decision-making process is a mechanism, where we determine the best choice from the possible objectives. The decision-making process has been extended to many fields such as supply chain management [2], improving the quality of hospital services [7] and railway facility choice, [19], etc. It is very difficult to collect accurate and relevant information about the socio-economic environment due to the increasing complexity and ambiguity of data collection. Real-world decision-making on the basis of accurate data information is required to find the optimal alternative and we use some different models for real-world decisionmaking. The classical methods failed to explain the complexity of real-life decision-making problems due to unclear and ambiguous information. The ambiguous and uncertain information requires several new techniques to clarify the actual decision-making process. To overcome
Muhammad Naeem [email protected] 1
Department of Mathematics, Abdul Wali Khan University Mardan, Mardan, Pakistan
2
Algebra and Applications Research Unit, Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
3
Centre of Excellence in Mathemat
Data Loading...
