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2-tuple linguistic Fermatean fuzzy MAGDM based on the WASPAS method for selection of solid waste disposal location

Authors :
Muhammad Akram
Usman Ali
Gustavo Santos-García
Zohra Niaz
Source :
Mathematical Biosciences and Engineering, Vol 20, Iss 2, Pp 3811-3837 (2023)
Publication Year :
2023
Publisher :
AIMS Press, 2023.

Abstract

Manufacturing plants generate toxic waste that can be harmful to workers, the population and the atmosphere. Solid waste disposal location selection (SWDLS) for manufacturing plants is one of the fastest growing challenges in many countries. The weighted aggregated sum product assessment (WASPAS) is a unique combination of the weighted sum model and the weighted product model. The purpose of this research paper is to introduce a WASPAS method with a 2-tuple linguistic Fermatean fuzzy (2TLFF) set for the SWDLS problem by using the Hamacher aggregation operators. As it is based on simple and sound mathematics, being quite comprehensive in nature, it can be successfully applied to any decision-making problem. First, we briefly introduce the definition, operational laws and some aggregation operators of 2-tuple linguistic Fermatean fuzzy numbers. Thereafter, we extend the WASPAS model to the 2TLFF environment to build the 2TLFF-WASPAS model. Then, the calculation steps for the proposed WASPAS model are presented in a simplified form. Our proposed method, which is more reasonable and scientific in terms of considering the subjectivity of the decision maker's behaviors and the dominance of each alternative over others. Finally, a numerical example for SWDLS is proposed to illustrate the new method, and some comparisons are also conducted to further illustrate the advantages of the new method. The analysis shows that the results of the proposed method are stable and consistent with the results of some existing methods.

Details

Language :
English
ISSN :
15510018
Volume :
20
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Mathematical Biosciences and Engineering
Publication Type :
Academic Journal
Accession number :
edsdoj.0de3a1ad7ac54230b445a9efc8d34634
Document Type :
article
Full Text :
https://doi.org/10.3934/mbe.2023179?viewType=HTML