An enhanced MADM with L q * q-Rung orthopair multi-fuzzy soft set in healthcare supplier selection

Abstract

The global healthcare systems have encountered unparalleled difficulties due to the COVID-19 pandemic, underscoring the crucial significance of effective management within healthcare supply chains. This research contributes to the field of healthcare supply chain management by presenting a robust MADM methodology called lattice ordered(L_{q
^*}) q-rung orthopair multi-fuzzy soft set(L_{q
^*} q-RO_MFS-MADM) for supplier evaluation and ranking amidst the challenges posed by the COVID-19 pandemic. Taking inspiration from multi-fuzzy soft set and q-rung orthopair fuzzy set, the present research article proposes a novel framework known as L_{q
^*} q-rung orthopair multi-fuzzy soft set (L_{q
^*} qRO_MFSS), which incorporates lattice ordering in q-rung orthopair multi-fuzzy soft set. The effectiveness of the proposed model is confirmed through successful experimentation on various important operations, including union, intersection, complement, restricted union and intersection. Moreover, the verification of De Morgan’s laws for L_{q
^*} qRO_MFSS is carried out specifically for these operations mentioned above. To highlight the significance of the proposed L_{q
^*} qRO_MFSS, a multi-attribute decision-making (MADM) problem is presented, showcasing its application in the domain of healthcare supply chain management. Furthermore, a comparative analysis is conducted to elucidate the advantages of this model in comparison to existing models.

Keywords

Lattice ordered multi-fuzzy soft set q-rung orthopair multi-fuzzy soft set supplier selection multi-attribute decision-making

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