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Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

Ahmed B. Khoshaim1, Saleem Abdullah2, Shahzaib Ashraf3,*, Muhammad Naeem4

1 Department of Mechanical Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
2 Department of Mathematics, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan
3 Department of Mathematics and Statistics, Bacha Khan University, Charsadda, 24420, Pakistan
4 Deanship of Combined First Year, Umm Al-Qura University, Makkah, Saudi Arabia

* Corresponding Author: Shahzaib Ashraf. Email: email

(This article belongs to the Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)

Computers, Materials & Continua 2021, 69(3), 4077-4094.


With the frequent occurrences of emergency events, emergency decision making (EDM) plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times. It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time, since inappropriate decisions may result in enormous economic losses and social disorder. To handle emergency effectively and quickly, this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough (q-ROPR) set. A novel list of q-ROFR aggregation information, detailed description of the fundamental characteristics of the developed aggregation operators and the q-ROFR entropy measure that determine the unknown weight information of decision makers as well as the criteria weights are specified. Further an algorithm is given to tackle the uncertain scenario in emergency to give reliable and reasonable emergency decisions. By using proposed list of q-ROFR aggregation information all emergency alternatives are ranked to get the optimal one. Besides this, the q-ROFR entropy measure method is used to determine criteria and experts’ weights objectively in the EDM process. Finally, through an illustrative example of COVID-19 analysis is compared with existing EDM methods. The results verify the effectiveness and practicability of the proposed methodology.


Cite This Article

A. B. Khoshaim, S. Abdullah, S. Ashraf and M. Naeem, "Emergency decision-making based on q-rung orthopair fuzzy rough aggregation information," Computers, Materials & Continua, vol. 69, no.3, pp. 4077–4094, 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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