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The COVID-19 pandemic and the performance of healthcare supply chains
by
Matin, Reza Kazemi
, Azadi, Majid
, Cheng, T. C. E
, Saen, Reza Farzipoor
in
Convexity
/ COVID-19
/ Data envelopment analysis
/ Disaster management
/ Health care
/ Operations research
/ Outbreaks
/ Pandemics
/ Performance enhancement
/ Performance evaluation
/ Supply chains
/ Viral diseases
2024
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The COVID-19 pandemic and the performance of healthcare supply chains
by
Matin, Reza Kazemi
, Azadi, Majid
, Cheng, T. C. E
, Saen, Reza Farzipoor
in
Convexity
/ COVID-19
/ Data envelopment analysis
/ Disaster management
/ Health care
/ Operations research
/ Outbreaks
/ Pandemics
/ Performance enhancement
/ Performance evaluation
/ Supply chains
/ Viral diseases
2024
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Do you wish to request the book?
The COVID-19 pandemic and the performance of healthcare supply chains
by
Matin, Reza Kazemi
, Azadi, Majid
, Cheng, T. C. E
, Saen, Reza Farzipoor
in
Convexity
/ COVID-19
/ Data envelopment analysis
/ Disaster management
/ Health care
/ Operations research
/ Outbreaks
/ Pandemics
/ Performance enhancement
/ Performance evaluation
/ Supply chains
/ Viral diseases
2024
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The COVID-19 pandemic and the performance of healthcare supply chains
Journal Article
The COVID-19 pandemic and the performance of healthcare supply chains
2024
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Overview
Recent pandemic outbreaks, including the COVID-19 and SARS, have revealed that supply chains (SCs) are unable to respond to such disasters. To mitigate the destructive impacts and improve the performance of SCs, Operations Research (OR) techniques have been applied to address the issues over the last two decades. The objective of this paper is to develop a network data envelopment analysis (NDEA) model to measure the resilience and sustainability of healthcare SCs in response to the COVID-19 pandemic outbreak. In the proposed NDEA model, for the first time, outputs’ weak disposability, chance-constrained programming (CCP), the convexity assumption, and the semi-oriented radial approach are aggregated. Moreover, a modified directional distance function (DDF) measure is developed to measure the overall and divisional efficiency scores. Furthermore, the proposed model can deal with different types of data such as integer-valued data, negative data, stochastic data, ratio data, and undesirable outputs. Also, several useful and interesting properties of the novel efficiency measure are presented. Finally, we measure the performance of 28 healthcare SCs to demonstrate the applicability and capability of our proposed approach.
Publisher
Springer Nature B.V
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