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Applications of the Multiattribute Decision-Making for the Development of the Tourism Industry Using Complex
Abrar Hussain1, Kifayat Ullah1, Jihad Ahmad2
1Department of Mathematics, Riphah International University (Lahore Campus), Lahore 54000, Pakistan.
Computational Intelligence and Neuroscience
|October 20, 2022
Summary
This study introduces new Hamy mean operators for complex intuitionistic fuzzy sets, enhancing uncertain information aggregation. These operators improve accuracy in multi-attribute decision-making, particularly for tourism destination analysis.
Area of Science:
- Decision Sciences
- Information Science
- Fuzzy Mathematics
Background:
- Aggregation of uncertain information requires considering interrelationships.
- Hamy mean (HM) is a tool for handling such scenarios.
- Complex intuitionistic fuzzy sets (CIFSs) manage truth and falsity degrees.
Purpose of the Study:
- Extend Hamy mean (HM) and dual HM (DHM) operators within the CIFS framework.
- Introduce novel HM operators for CIF information: CIFHM, CIFWHM, CIFDHM, CIFWDHM.
- Apply these operators to multi-attribute decision-making (MADM) problems.
Main Methods:
- Development of four new HM operators for CIFSs.
- Numerical validation of the proposed HM operators.
- Construction and application of a MADM algorithm using the proposed operators.
Main Results:
- The proposed HM operators are numerically validated.
- The operators were successfully applied to a tourism destination selection MADM problem.
- Sensitivity analysis of parameters was performed.
Conclusions:
- The developed HM operators effectively handle uncertain information within CIFSs.
- The proposed MADM method using CIFSs and HM operators enhances result accuracy.
- The approach demonstrates effectiveness and flexibility for decision-making problems.
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