A fair bed allocation during COVID-19 pandemic using TOPSIS technique based on correlation coefficient for...

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.