How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis

Saeed Yousefi, Hadi Shabanpour, Kian Ghods et Reza Farzipoor Saen

Article de revue (2023)

Un lien externe est disponible pour ce document

Département:	Département de mathématiques et de génie industriel
URL de PolyPublie:	https://publications.polymtl.ca/53306/
Titre de la revue:	Computers & Industrial Engineering (vol. 176)
Maison d'édition:	Elsevier
DOI:	10.1016/j.cie.2022.108933
URL officielle:	https://doi.org/10.1016/j.cie.2022.108933
Date du dépôt:	18 avr. 2023 14:58
Dernière modification:	08 avr. 2025 07:22

Citer en APA 7:	Yousefi, S., Shabanpour, H., Ghods, K., & Saen, R. F. (2023). How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis. Computers & Industrial Engineering, 176, 108933 (11 pages). https://doi.org/10.1016/j.cie.2022.108933
APA 7 (HTML)ASCII CitationAtomBibTeXDOIsDublin CoreEndNote (.ENW)MARC (ISO 2709/UTF-8) (.MRC)MARC (UTF-8) (.MRK8)MARC XMLMPEG-21 DIDLMultiline CSVOAI-ORE Resource Map (Atom Format)OPENAIREOpenURL ContextObjectOpenURL ContextObject in SpanRDF+N-TriplesRDF+N3RDF+XMLReferReference Manager (.RIS)Simple Metadata

Statistiques

Dimensions

Actions réservées au personnel

Afficher document