Simic Vladimir, Ebadi Torkayesh Ali, Ijadi Maghsoodi Abtin
Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11010 Belgrade, Serbia.
School of Business and Economics, RWTH Aachen University, 52072 Aachen, Germany.
Ann Oper Res. 2022 Jul 8:1-46. doi: 10.1007/s10479-022-04822-0.
Hazardous healthcare waste (HCW) management system is one of the most critical urban systems affected by the COVID-19 pandemic due to the increase in waste generation rate in hospitals and medical centers dealing with infected patients as well as the degree of hazardousness of generated waste due to exposure to the virus. In this regard, waste network flow would face severe problems without taking care of hazardous waste through disinfection facilities. For this purpose, this study aims to develop an advanced decision support system based on a multi-stage model that was combined with the random forest recursive feature elimination (RF-RFE) algorithm, the indifference threshold-based attribute ratio analysis (ITARA), and measurement of alternatives and ranking according to compromise solution (MARCOS) methods into a unique framework under the Fermatean fuzzy environment. In the first stage, the innovative Fermatean fuzzy RF-RFE algorithm extracts core criteria from a finite set of initial criteria. In the second stage, the novel Fermatean fuzzy ITARA determines the semi-objective importance of the core criteria. In the third stage, the new Fermatean fuzzy MARCOS method ranks alternatives. A real-life case study in Istanbul, Turkey, illustrates the applicability of the introduced methodology. Our empirical findings indicate that "Pendik" is the best among five candidate locations for sitting a new disinfection facility for hazardous HCW in Istanbul. The sensitivity and comparative analyses confirmed that our approach is highly robust and reliable. This approach could be used to tackle other critical multi-dimensional problems related to COVID-19 and support sustainability and circular economy.
The online version contains supplementary material available at 10.1007/s10479-022-04822-0.
危险医疗废物（HCW）管理系统是受新冠疫情影响最关键的城市系统之一，这是由于收治感染患者的医院和医疗中心的废物产生率增加，以及因接触病毒而产生的废物的危险程度所致。在这方面，如果不通过消毒设施处理危险废物，废物网络流程将面临严重问题。为此，本研究旨在开发一种基于多阶段模型的先进决策支持系统，该模型将随机森林递归特征消除（RF-RFE）算法、基于无差异阈值的属性比率分析（ITARA）以及基于折衷解的多准则决策方法（MARCOS）结合在费马模糊环境下的一个独特框架中。在第一阶段，创新的费马模糊RF-RFE算法从有限的初始准则集中提取核心准则。在第二阶段，新颖的费马模糊ITARA确定核心准则的半客观重要性。在第三阶段，新的费马模糊MARCOS方法对备选方案进行排序。土耳其伊斯坦布尔的一个实际案例研究说明了所介绍方法的适用性。我们的实证结果表明，“彭迪克”是伊斯坦布尔五个候选地点中设置危险医疗废物新消毒设施的最佳地点。敏感性和比较分析证实，我们的方法具有高度的稳健性和可靠性。该方法可用于解决与新冠疫情相关的其他关键多维度问题，并支持可持续性和循环经济。
在线版本包含可在10.1007/s10479-022-04822-0获取的补充材料。
