COVID19 epidemic outbreak: operating rooms scheduling, specialty teams timetabling and emergency patients' assignment using the robust optimization approach

Momeni, Mojtaba Arab; Mostofi, Amirhossein; Jain, Vipul; Soni, Gunjan

doi:10.1007/s10479-022-04667-7

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…Also, it affected job scheduling and timetabling. People changed their work routines (Arab Momeni et al, 2022 ). It has become the most popular topic argued on the internet (Kumar et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Gülmez

2022

Ann Oper Res

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The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.

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Section: Introductionmentioning

confidence: 99%

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Gülmez

2022

Ann Oper Res

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“…In order to provide solutions to the arising problems, "digital technologies" such as "blockchain" [106][107][108][109], "artificial intelligence" [50,110,111], and "big data" [112,113] have been explored. Moreover, uncertain conditions have encouraged implementing "robust optimization" [63,114].…”

Section: Title Analysismentioning

confidence: 99%

Bibliometric and Text Analytics Approaches to Review COVID-19 Impacts on Supply Chains

2022

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The current COVID-19 pandemic has virtually disrupted supply chains worldwide. Thus, supply chain research has received significant attention. While the impacts have been immeasurable, organizations have realized the need to design strategies to overcome such unexpected events. Therefore, the supply chain research landscape has evolved to address the challenges during the pandemic. However, available literature surveys have not explored the power of text analytics. Hence, in this review, an analysis of the supply chain literature related to the impacts of COVID-19 is performed to identify the current research trends and future research avenues. To discover the frequent topics discussed in the literature, bibliometric analysis (i.e., keyword co-occurrence network) and text mining tools (i.e., N-gram analysis and topic modeling) are employed for the whole corpus and the top-three contributing journals (i.e., Sustainability, International Journal of Logistics Management, Operations Management Research). Moreover, text analytics (i.e., Term Frequency-Inverse Document Frequency: TF-IDF) is utilized to discover the distinctive topics in the corpus and per journals. A total of 574 papers published up to the first semester of 2022 were collected from the Scopus database to determine the research trends and opportunities. The keyword network identified four clusters considering the implementation of digitalization to achieve resilience and sustainability, the usage of additive manufacturing during the pandemic, the study of food supply chains, and the development of supply chain decision models to tackle the pandemic. Moreover, the segmented keyword network analysis and topic modeling were performed for the top three contributors. Although both analyses draw the research concentrations per journal, the keyword network tends to provide a more general scope, while the topic modeling gives more specific topics. Furthermore, TF-IDF scores unveiled topics rarely studied, such as the implications of the pandemic on plasma supply chains, cattle supply chains, and reshoring decisions, to mention a few. Additionally, it was observed how the different methodologies implemented allowed to complement the information provided by each method. Based on the findings, future research avenues are discussed. Therefore, this research will help supply chain practitioners and researchers to identify supply chain advancements, gaps in the literature and future research streams.

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“…Operating rooms were repurposed for intensive care units (ICU) or COVID-19 treatment areas, limiting the availability of facilities for surgeries. 4 , 22 In this circumstance, the primary objective of this study is to assess the impact of COVID-19 on elective and emergency surgeries in a Brazilian metropolitan area. The secondary objective is to compare the postoperative hospital mortality before and after the pandemic.…”

Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 Pandemic on Elective and Emergency Surgeries, and Postoperative Mortality in a Brazilian Metropolitan Area: A Time-Series Cohort Study

Ferreira,

Bolognani,

Santana

et al. 2024

RMHP

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COVID19 epidemic outbreak: operating rooms scheduling, specialty teams timetabling and emergency patients' assignment using the robust optimization approach

Cited by 7 publications

References 52 publications

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images

Bibliometric and Text Analytics Approaches to Review COVID-19 Impacts on Supply Chains

Impact of the COVID-19 Pandemic on Elective and Emergency Surgeries, and Postoperative Mortality in a Brazilian Metropolitan Area: A Time-Series Cohort Study

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