A fair bed allocation during COVID-19 pandemic using TOPSIS technique based on correlation coefficient for interval-valued pythagorean fuzzy hypersoft set
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces correlation coefficients for interval-valued Pythagorean fuzzy hypersoft sets (IVPFHSS), enhancing statistical analysis in complex scenarios. These new measures improve decision-making, as demonstrated by optimizing hospital bed allocation during the COVID-19 pandemic.
Area Of Science
- Statistics and Decision Science
- Fuzzy Set Theory
- Data Analysis
Background
- Statistical analysis accuracy relies on data quality, which can be unclear or difficult to interpret.
- Traditional correlation coefficients are not commonly applied to interval-valued Pythagorean fuzzy hypersoft sets (IVPFHSS).
- IVPFHSS offers a generalized framework for more precise and accurate data analysis.
Purpose Of The Study
- To introduce correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS.
- To explore the essential properties of these newly defined correlation measures.
- To demonstrate the practical application of CC and WCC in decision-making, specifically for hospital bed allocation during the COVID-19 pandemic using a TOPSIS model.
Main Methods
- Development of correlation coefficient (CC) and weighted correlation coefficient (WCC) tailored for IVPFHSS.
- Application of a Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) model for prioritization.
- Numerical investigations including sensitivity analyses to evaluate decision structures.
Main Results
- The study successfully defined and explored properties of CC and WCC for IVPFHSS.
- The proposed methodology was applied to optimize hospital bed allocation during the COVID-19 pandemic, demonstrating its effectiveness.
- The developed algorithm showed more consistent efficiency compared to prevalent models in determining optimal configurations.
Conclusions
- The integration of correlation measures within IVPFHSS provides valuable insights for decision-making in uncertain environments.
- The developed multi-attribute decision-making (MADM) methodology is robust and significant for complex problems.
- Future work includes developing a dynamic bed allocation algorithm based on biogeography for enhanced decision systems.
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