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Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19

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dc.contributor.author Kang, Daekook
dc.contributor.author Devi, S. Aicevarya
dc.contributor.author Felix, Augustin
dc.contributor.author Narayanamoorthy, Samayan
dc.contributor.author Kalaiselvan, Samayan
dc.contributor.author Baleanu, Dumitru
dc.contributor.author Ahmadian, Ali
dc.date.accessioned 2024-03-19T12:47:12Z
dc.date.available 2024-03-19T12:47:12Z
dc.date.issued 2022-01
dc.identifier.citation Kang, Daekook;...et.al. "Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19", Operations Research Perspectives, Vol.9. tr_TR
dc.identifier.issn 22147160
dc.identifier.uri http://hdl.handle.net/20.500.12416/7633
dc.description.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 ado tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1016/j.orp.2022.100258 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject COVID-19 tr_TR
dc.subject Service Robots tr_TR
dc.subject Trapezoidal Intuitionistic Fuzzy Number tr_TR
dc.subject FDM tr_TR
dc.subject SBWM tr_TR
dc.subject MAUT Method tr_TR
dc.subject CTrIFCS Algorithm tr_TR
dc.title Intuitionistic fuzzy MAUT-BW Delphi method for medication service robot selection during COVID-19 tr_TR
dc.type article tr_TR
dc.relation.journal Operations Research Perspectives tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 9 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü tr_TR


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