2-Tuple Linguistic Fermatean Fuzzy Decision-Making Method Based on COCOSO with CRITIC for Drip Irrigation System Analysis
DOI:
https://doi.org/10.47852/bonviewJCCE2202356Keywords:
2-tuple linguistic model, Fermatean fuzzy sets, CoCoSo, Hamacher aggregation operatorAbstract
Given the increasing scarcity of water resources, especially climate change, the adoption of water-efficient irrigation systems (ISs) is becoming increasingly important. Drip irrigation systems (DISs) are the most successful method of saving water and increasing agricultural yields in water-efficient IS. DIS reduces not only the cost of water supply but also the cost of activities such as labor costs and other planting costs. DIS is the most reliable, profitable, and cost-effective agricultural irrigation technique for the vast majority of crops, and it could be a potential solution to the growing water crisis caused by climate change. The Hamacher operation is an extension of the algebraic and Einstein operations. The combination of 2-tuple linguistic Fermatean fuzzy (2TLFF) numbers and the Hamacher operation is more valuable and agile. The method based on the Combined Compromise Solution (CoCoSo) with Criteria Importance Through Intercriteria Correlation (CRITIC) is introduced to manage multiple attribute group decision-making (MAGDM) issues in a 2TLFF environment. Finally, a practical example is shown, followed by a comparison study that supports the unique approach’s efficacy and generalizability. The suggested method distinguishes itself by having no paradoxical instances and a powerful ability for recognizing the optimal choice.
Received: 2 August 2022 | Revised: 4 September 2022 | Accepted: 12 September 2022
Conflicts of Interest
Muhammad Akram is an Editorial Board Member for Journal of Computational and Cognitive Engineering, and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data available on request from the corresponding author upon reasonable request.
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