Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19
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 f...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Operations Research Perspectives |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221471602200029X |
| _version_ | 1811296783436873728 |
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| author | Daekook Kang S. Aicevarya Devi Augustin Felix Samayan Narayanamoorthy Samayan Kalaiselvan Dumitru Balaenu Ali Ahmadian |
| author_facet | Daekook Kang S. Aicevarya Devi Augustin Felix Samayan Narayanamoorthy Samayan Kalaiselvan Dumitru Balaenu Ali Ahmadian |
| author_sort | Daekook Kang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:53:37Z |
| format | Article |
| id | doaj.art-f3790cf0d20d4f5eaf71f9a438d7aff1 |
| institution | Directory Open Access Journal |
| issn | 2214-7160 |
| language | English |
| last_indexed | 2024-04-13T05:53:37Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Operations Research Perspectives |
| spelling | doaj.art-f3790cf0d20d4f5eaf71f9a438d7aff12022-12-22T02:59:41ZengElsevierOperations Research Perspectives2214-71602022-01-019100258Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19Daekook Kang0S. Aicevarya Devi1Augustin Felix2Samayan Narayanamoorthy3Samayan Kalaiselvan4Dumitru Balaenu5Ali Ahmadian6Department of Industrial and Management Engineering, Institute of Digital Anti-Aging Health Care, Inje University, 197 Inje-ro, Gimhae-si, Gyeongsangnam-do, 50834, Republic of KoreaMathematics Division, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, IndiaMathematics Division, School of Advanced Sciences, Vellore Institute of Technology, Chennai Campus, IndiaDepartment of Mathematics, Bharathiar University, Coimbatore 46, India; Corresponding authors.Department of Social Work, SRMV College of Arts and Science, Coimbatore 641020, IndiaDepartment of Mathematics, Cankaya University, 06530 Balgat, Ankara, Turkey; Lebanese American University, 11022801, Beirut, Lebanon; Department of Medical Research, China Medical University, Taichung 40402, Taiwan; Corresponding authors.Decision Lab, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy; Department of Mathematics, Near East University, Nicosia, TRNC, Mersin 10, TurkeyCoronavirus 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.http://www.sciencedirect.com/science/article/pii/S221471602200029XCOVID-19, Service robots, Trapezoidal intuitionistic fuzzy number, FDM, SBWM, MAUT method, CTrIFCS algorithm |
| spellingShingle | Daekook Kang S. Aicevarya Devi Augustin Felix Samayan Narayanamoorthy Samayan Kalaiselvan Dumitru Balaenu Ali Ahmadian Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19 Operations Research Perspectives COVID-19, Service robots, Trapezoidal intuitionistic fuzzy number, FDM, SBWM, MAUT method, CTrIFCS algorithm |
| title | Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19 |
| title_full | Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19 |
| title_fullStr | Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19 |
| title_full_unstemmed | Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19 |
| title_short | Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19 |
| title_sort | intuitionistic fuzzy maut bw delphi method for medication service robot selection during covid 19 |
| topic | COVID-19, Service robots, Trapezoidal intuitionistic fuzzy number, FDM, SBWM, MAUT method, CTrIFCS algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S221471602200029X |
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