MultiFuzzTOPS: A Fuzzy Multi-Criteria Decision-Making Model Using Type-2 Soft Sets and TOPSIS

Author:

Manzoor Shumaila1,Mustafa Saima2ORCID,Gulzar Kanza3ORCID,Gulzar Asim4,Kazmi Sadia Nishat5,Akber Syed Muhammad Abrar6ORCID,Bukhsh Rasool7,Aslam Sheraz8ORCID,Mohsin Syed Muhammad79ORCID

Affiliation:

1. Department of Mathematics and Statistics, PMAS-Arid Agriculture University, Rawalpindi 46000, Pakistan

2. Department of Mathematics, Rawalpindi Women University, Rawalpindi 46000, Pakistan

3. University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi 46000, Pakistan

4. Department of Entomology, PMAS-Arid Agriculture University, Rawalpindi 46000, Pakistan

5. Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland

6. Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland

7. Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan

8. Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus

9. College of Intellectual Novitiates (COIN), Virtual University of Pakistan, Lahore 55150, Pakistan

Abstract

Effective and optimal decision-making can enhance system performance, potentially leading to a positive reputation and financial gains. Multi-criteria decision-making (MCDM) is an important research topic widely applied to practical decision-making problems. Using the basic idea of symmetry to balance the arrangement where elements or features have an equality or similarity in distribution, MCDM provides robust decisions in such multi-dimensional complex issues. This study proposes MultiFuzzTOPS, a decision-making model to deal with complexity of multi-criteria decision-making. The proposed MultiFuzzTOPS leverages the fuzzy logic and soft sets such as type-2 soft sets (T2SS) and technique for order preference by similarity to ideal solution (TOPSIS) for decision-making. We validate the proposed model by implementing it to solve the pesticide selection problem in food science by considering various criteria for the selection of pesticides. Our proposed MultiFuzzTOPS recommends the best pesticide compared with its counterparts because it covers the maximum information for the selection of the best alternative. Results are ranked on the basis of the Hamming distance and similarity coefficient. We also validate the effectiveness by performing the sensitivity analysis, and the validation shows the reliability and effectiveness of our proposed model.

Publisher

MDPI AG

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