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Inverse data envelopment analysis for two-stage network systems with uncertain inputs and uncertain undesirable outputs
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چکیده: (41 مشاهده) |
Evaluation of healthcare systems, as a key organization providing different health services, is essential. This issue becomes more crucial when occurring crises such as a pandemic. They need to keep track of their success in the face of the crisis to assess the effects of policy changes and their capability to respond to new challenges. The inverse data envelopment analysis (InvDEA) technique is an applicable method in order to estimate the input/output levels of decision-making units (DMUs) to preserve predetermined technical efficiency scores. In classic studies of InvDEA, decision-Making Units (DMUs) as black boxes, ignoring their internal structure. This paper estimates input levels and new intermediate products to achieve a predetermined efficiency score set by the decision maker. In traditional inverse data envelopment analysis models, precise data are required to determine the input and/or output levels of each decision-making unit. However, in many scenarios, such as system flexibility, social and cultural contexts information may be indeterminate. In these cases, experts’ opinions are used to model uncertainty. Uncertainty theory, a branch of mathematics, logically deals with degrees of belief. This paper aims to develop an inverse Network DEA model incorporating uncertainty theory. We assume that inputs and outputs of decision-making units are based on experts’ belief degrees. To demonstrate the model is performance, we explore efficiency of healthcare systems during COVID-19 pandemic.
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متن کامل [PDF 643 kb]
(12 دریافت)
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نوع مطالعه: پژوهشی |
موضوع مقاله:
Decision Analysis and Decision Support Systems
دریافت: 1404/9/14 | پذیرش: 1404/11/9 | انتشار: 1404/11/9
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