基于直觉模糊mut - bw德尔菲法的COVID-19用药服务机器人选择

IF 3.7 4区 管理学 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Daekook Kang , S. Aicevarya Devi , Augustin Felix , Samayan Narayanamoorthy , Samayan Kalaiselvan , Dumitru Balaenu , Ali Ahmadian
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引用次数: 8

摘要

2019冠状病毒病(COVID-19)是由冠状病毒家族成员新型冠状病毒引起的一种新疾病,目前对所有人构成威胁,已成为医疗保健机构面临的重大挑战。除其他策略外,还使用机器人技术来降低COVID - 19的死亡率和全球传播率。机器人外形酷似人体,是一种可编程的机械装置。由于新冠肺炎是高度传染性疾病,因此决定通过药物服务机器人(MSR)来管理危重期患者的治疗。服务机器人的使用减少了感染的传播和人为失误,并防止一线医护人员直接接触COVID - 19疾病。在不同的备选方案中选择最合适的机器人可能是复杂的。因此,需要一些数学工具来进行适当的选择。因此,本研究设计了mot - bw德尔菲法,采用综合模糊MCDM方法分析治疗新冠肺炎患者的MSR选择,并根据影响标准对这些备选方案进行排序。梯形直觉模糊数是表达模糊信息的一种有效方法,利用梯形直觉模糊数转换成清晰分数(CTrIFCS)算法对其进行去模糊化。采用模糊德尔菲法(FDM)选择最合适的评价标准,并采用简化的最佳-最差评价法(SBWM)对评价标准进行加权。在多属性效用理论(MAUT)方法下,研究了备选方案与标准之间的性能关系。此外,为了评估所提出方法的有效性,与现有的去模糊化技术和距离测量进行了灵敏度和比较分析。本研究还采用了相关检验的思想来比较不同去模糊化方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
Operations Research Perspectives
Operations Research Perspectives Mathematics-Statistics and Probability
CiteScore
6.40
自引率
0.00%
发文量
36
审稿时长
27 days
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