Selection of healthcare waste disposal strategies with L q * q-rung orthopair multi-fuzzy soft set similarity measures

Abstract

The global challenges associated with urbanization and the escalating waste production have been magnified in recent times, particularly in the context of the COVID-19 pandemic. In response to these challenges, municipal authorities, especially in developing nations, are confronted with the imperative task of discerning the most suitable healthcare waste (HCW) disposal methods. These methods are crucial for the effective management of medical waste, both during and after the COVID-19 era. This study introduces a novel similarity measure designed for lattice ordered q-rung orthopair multi-fuzzy soft sets (L_{q
^*} q-ROMⁿFSSs) and exploring some of their essential characteristics. Currently, no established methods are available for gauging the similarity of L_{q
^*} q-ROMⁿFSSs sets. Therefore, this paper takes a pioneering step by presenting similarity measures tailored for L_{q
^*} q-ROMⁿFSSs sets. Moreover, we propose an evaluation methodology that leverages the lattice ordered q-rung orthopair multi-fuzzy soft information to determine the optimal health care waste (HCW) disposal approach. This approach seeks to enhance decision-making within the realm of waste management, facilitating more informed and effective choices in handling healthcare waste.

Keywords

Multi-fuzzy soft set healthcare waste disposal technique

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