利用 OWCM-CODAS 和三角中性集评估和选择医疗废物处理设备的系统方法

Asmaa Elsayed, Bilal Arain
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引用次数: 0

摘要

医疗保健是人类生活的一个基本方面,影响着个人、社区和社会。投资于医疗保健基础设施、服务和教育对于培养健康、繁荣的人口至关重要。医疗废物管理是公共卫生的一个重要方面,需要医疗机构、政府和社区共同努力,确保废物管理方式能够最大限度地降低对人类健康和环境的风险。本文提出了一种混合方法,在三角中性集(TNS)框架内结合了意见权重标准法(OWCM)和基于距离的组合评估法(CODAS),以评估和选择最佳医疗废物处理设备。所提出的方法平衡了各种相互冲突的标准,并将不确定性纳入专家评估,以确保决策过程的稳健性。本文还对医疗废物管理设备进行了全面回顾,并强调了采用综合方法进行医疗废物管理的必要性。本文将所提出的方法应用于一项案例研究,根据经济、环境、技术和社会等不同因素对医疗废物处理设备进行评估。结果表明,与 TNS 相结合的 OWCM-CODAS 方法可根据多个标准对医疗废物处理设备进行有效评估和排名。敏感性分析表明了建议模型的稳健性,结果表明,在测试范围内,模型的性能和替代方案的相对排序对阈值参数 τ 的变化并不敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Systematic Approach for Evaluating and Selecting Healthcare Waste Treatment Devices using OWCM-CODAS and Triangular Neutrosophic Sets
Healthcare is a fundamental aspect of human life, impacting individuals, communities, and societies. Investing in healthcare infrastructure, services, and education is essential for fostering a healthy, thriving population. Healthcare waste management is a critical aspect of public health, and it requires a concerted effort from healthcare facilities, governments, and communities to ensure that waste is managed in a way that minimizes risks to human health and the environment. This paper proposes a hybrid methodology combining the Opinion Weight Criteria Method (OWCM) and the Combinative Distance-Based Assessment (CODAS) within the framework of Triangular Neutrosophic Sets (TNS) to evaluate and select the optimal healthcare waste treatment devices. The proposed methodology balances various conflicting criteria and incorporates uncertainty in expert assessments to ensure a robust decision-making process. The paper also provides a comprehensive review of healthcare waste management devices and highlights the need for a comprehensive approach to healthcare waste management. The proposed methodology is applied to a case study involving evaluating healthcare waste treatment devices based on different factors such as economic, environmental, technological, and social aspects. The results show that the OWCM-CODAS methodology, integrated with TNS, effectively evaluates and ranks healthcare waste treatment devices based on multiple criteria. The sensitivity analysis demonstrates the robustness of the proposed model, and the results indicate that the model's performance and the relative ranking of the alternatives are not sensitive to changes in the threshold parameter τ within the tested range.
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