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dc.contributor.authorGÜNERİ, Ali Fuat
dc.contributor.authorGul, Muhammet
dc.contributor.authorGunal, Murat M.
dc.date.accessioned2022-07-04T16:15:53Z
dc.date.available2022-07-04T16:15:53Z
dc.date.issued2020
dc.identifier.citationGul M., GÜNERİ A. F. , Gunal M. M. , "Emergency department network under disaster conditions: The case of possible major Istanbul earthquake", JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, cilt.71, sa.5, ss.733-747, 2020
dc.identifier.issn0160-5682
dc.identifier.otherav_dc236890-9bb1-4252-9daa-d8fa900f36df
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/184964
dc.identifier.urihttps://doi.org/10.1080/01605682.2019.1582588
dc.description.abstractEmergency departments (EDs) provide health care services to people in need of urgent care. Their role is remarkable when extraordinary events that affect the public, such as earthquakes, occur. In this paper, we present a hybrid framework to evaluate earthquake preparedness of EDs in cities. Our hybrid framework uses artificial neural networks (ANNs) to estimate number of casualties and discrete event simulation (DES) to analyse the effect of surge in patient demand in EDs, after an earthquake happens. At the core of our framework, Earthquake Time Emergency Department Network Simulation Model (ET-EDNETSIM) resides which can simulate patient movements in a network of multiple and coordinated EDs. With the design of simulation experiments, different resource levels and sharing rules between EDs can be evaluated. We demonstrated our framework in a network of five EDs located in a region of which is estimated to have the highest injury rate after an earthquake in Istanbul, Turkey. Results of our study contributed to the planning for expected earthquake in Istanbul. Simulating a network of EDs extends the individual ED studies in the literature and furthermore, our hybrid framework can help increase earthquake preparedness in cities around the world. On the methodological side, the use of ANN, which is a member of machine learning (ML) algorithms family, in our hybrid framework also shows the close links between ML and DES.
dc.language.isoeng
dc.subjectSosyal ve Beşeri Bilimler
dc.subjectEkonometri
dc.subjectYöneylem
dc.subjectÇalışma Ekonomisi ve Endüstri ilişkileri
dc.subjectYönetim ve Çalışma Psikolojisi
dc.subjectGeneral Decision Sciences
dc.subjectDecision Sciences (miscellaneous)
dc.subjectManagement Science and Operations Research
dc.subjectOrganizational Behavior and Human Resource Management
dc.subjectSocial Sciences & Humanities
dc.subjectSosyal Bilimler (SOC)
dc.subjectEkonomi ve İş
dc.subjectYÖNETİM
dc.subjectOPERASYON ARAŞTIRMA VE YÖNETİM BİLİMİ
dc.titleEmergency department network under disaster conditions: The case of possible major Istanbul earthquake
dc.typeMakale
dc.relation.journalJOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
dc.contributor.departmentYıldız Teknik Üniversitesi , Makine Fakültesi , Endüstri Müh.Bölümü
dc.identifier.volume71
dc.identifier.issue5
dc.identifier.startpage733
dc.identifier.endpage747
dc.contributor.firstauthorID3407476


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