ABSTRACT
Coronavirus Disease 2019 (COVID-19), a new illness caused by a novel coronavirus, a member of the corona family of viruses, is currently posing a threat to all people, and it has become a significant challenge for healthcare organizations. Robotics are used among other strategies, to lower COVID’s fatality and spread rates globally. The robot resembles the human body in shape and is a programmable mechanical device. As COVID is a highly contagious disease, the treatment for the critical stage COVID patients is decided to regulate through medication service robots (MSR). The use of service robots diminishes the spread of infection and human error and prevents frontline healthcare workers from exposing themselves to direct contact with the COVID illness. The selection of the most appropriate robot among different alternatives may be complex. So, there is a need for some mathematical tools for proper selection. Therefore, this study design the MAUT-BW Delphi method to analyze the selection of MSR for treating COVID patients using integrated fuzzy MCDM methods, and these alternatives are ranked by influencing criteria. The trapezoidal intuitionistic fuzzy numbers are beneficial and efficient for expressing vague information and are defuzzified using a novel algorithm called converting trapezoidal intuitionistic fuzzy numbers into crisp scores (CTrIFCS). The most suitable criteria are selected through the fuzzy Delphi method (FDM), and the selected criteria are weighted using the simplified best-worst method (SBWM). The performance between the alternatives and criteria is scrutinized under the multi-attribute utility theory (MAUT) method. Moreover, to assess the effectiveness of the proposed method, sensitivity and comparative analyses are conducted with the existing defuzzification techniques and distance measures. This study also adopt the idea of a correlation test to compare the performance of different defuzzification methods.
ABSTRACT
The virus that arises from Wuhan, popularly called as “coronavirus” has been spread all over the world in a short period. India has also taken preventive measures to control this threatening virus. In addition to precautions, it is necessary to analyze the risk factors of COVID-19 in overpopulated countries to reduce the impact of the virus. As India is the second-populated country, analyzing the risk factor of COVID-19 helps in categorizing the likely and non-likely people affect by the virus. The work manages the fuzziness through intuitionistic fuzzy sets combine with the VIKOR decision-making process to find the most influencing risk factors of COVID-19. The objective weights of the criteria are evaluated by entropy as it measures the randomness in discrete distribution. Moreover, sensitivity analysis is conducted to verify the robustness of the results of the proposed method.