Triangular fuzzy merec (TFMEREC) and its applications in multi criteria decision making
نویسندگان
1 College of Computing, Informatics and Mathematics, UiTM Cawangan Negeri Sembilan, Kampus Seremban, 72000 Negeri Sembilan, Malaysia.
2 Department of Mathematics, Faculty of Education for Pure Sciences, University Of Anbar, Ramadi, Anbar, Iraq.
3 College of Pharmacy, National University of Science and Technology, Dhi Qar, Iraq.
4 College of Computing, Informatics and Mathematics, UiTM Cawangan Negeri Sembilan, Kampus Seremban, 72000 Negeri Sembilan, Malaysia.
5 Faculty Sains dan Technology, University Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan.
6 College of Computing, Informatics and Mathematics, UiTM Cawangan Selangor, Kampus Shah Alam, 40450 Shah Alam, Selangor Darul Ehsan Malaysia.
doi
10.22105/jfea.2024.446557.1399چکیده
This paper presents a proposed method, Triangular Fuzzy MEREC (TFMEREC), which combines Triangular Fuzzy Numbers (TFNs) and Method Based on The Removal Effects of Criteria (MEREC). This integration aims to create an efficient implementation process that provides an efficient solution for complex MCDM issues, focusing on assessing halal suppliers. To demonstrate the feasibility and effectiveness of TFMEREC, this study will use an illustrative example to employ three different normalization methods with three different distance methods. Key findings from this research show that TFMEREC improves the accuracy and reliability of criteria weight determination. TFMEREC gives decision-makers more accurate weights, allowing for more informed decision-making processes. Furthermore, the sensitivity analysis provides insights into the impact of various normalization and distance methods on overall results, which improves the method's applicability and reliability. The TFMEREC method is a promising approach for dealing with imprecise and uncertain information in decision-making contexts, with potential applications in various domains. Overall, the findings underline the importance of methodological breakthroughs in enhancing decision-making processes, and the study is relevant to both experts and researchers.
