TOPSIS and VIKOR strategies for COVID-19 vaccine selection in QNN environment

This paper intends to propose two Multi-attribute Group Decision-Making (MAGDM) methodologies based on TOPSIS and VIKOR strategies under QNN theory for assessing COVID-19 vaccine selection. The present work is mainly divided into four parts. The first part introduces a new entropy weight for Quadripartition Neutrosophic Number (QNNs) to calculate the weights of the attributes. The properties related to the new operation are discussed in detail. In the second part, we use these attribute weights to develop the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) strategies in the QNN environment. These two MAGDM strategies are put forward based on TOPSIS and VIKOR in the third part. These methodologies are applied in ranking different COVID-19 vaccines by considering each vaccine’s safety profile, potential for efficacy, stability, availability, cost, and implementation factors. In the fourth part, the reliability and effectiveness of the proposed methodologies are explored by comparing these two strategies.