MABAC under non-linear diophantine fuzzy numbers: A new approach for emergency decision support systems

Abstract

<abstract><p>The Covid-19 emergency condition is a critical issue for emergency decision support systems. Controlling the spread of Covid-19 in emergency circumstances throughout the global is a difficult task, hence the purpose of this research is to develop a non-linear diophantine fuzzy decision making mechanism for preventing and identifying Covid-19. Fundamentally, the article is divided into three sections in order to establish suitable and correct procedures to meet the circumstances of emergency decision-making. Firstly, we present a non-linear diophantine fuzzy set (non-LDFS), which is the generalisation of Pythagorean fuzzy set, q-rung orthopair fuzzy set, and linear diophantine fuzzy set, and explain their critical features. In addition, algebraic norms for non-LDFSs are constructed based on particular operational rules. In the second section, we use non-LDF averaging and geometric operator to aggregate expert judgements. The last section of this study consists of ranking in which MABAC (multi-attributive border approximation area comparison) method is used to handle the Covid-19 emergency circumstance using non-LDF information. Moreover, based on the presented methods, the numerical case-study of Covid-19 condition is presented as an application for emergency decision-making. The results shows the efficiency of our proposed techniques and give precise emergency strategies to resolve the worldwide ambiguity of Covid-19.</p></abstract>