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Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis

This paper deals with evaluating congestion in free disposal hull (FDH) models. There are several approaches in data envelopment analysis (DEA) literatures which discuss the theory and application of congestion. However, almost all of these approaches considered convex DEA technologies. So, in the c...

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Detalles Bibliográficos
Autores principales: Abbasi, M., Jahanshahloo, G. R., Rostamy-Malkhlifeh, M., Hosseinzadeh Lotfi, F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213404/
https://www.ncbi.nlm.nih.gov/pubmed/25379531
http://dx.doi.org/10.1155/2014/427673
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author Abbasi, M.
Jahanshahloo, G. R.
Rostamy-Malkhlifeh, M.
Hosseinzadeh Lotfi, F.
author_facet Abbasi, M.
Jahanshahloo, G. R.
Rostamy-Malkhlifeh, M.
Hosseinzadeh Lotfi, F.
author_sort Abbasi, M.
collection PubMed
description This paper deals with evaluating congestion in free disposal hull (FDH) models. There are several approaches in data envelopment analysis (DEA) literatures which discuss the theory and application of congestion. However, almost all of these approaches considered convex DEA technologies. So, in the case of nonconvex technologies, including FDH technology, this field is almost nil. This paper makes an attempt to fill in this void. To do so, this study provides a pairwise comparisons-based algorithm to evaluate congestion in FDH model. This algorithm identifies the sources of congestion and estimates its amounts. It is also capable of detecting the losses amounts of output due to congestion. The validity of the proposed model is demonstrated using some numerical and empirical examples.
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spelling pubmed-42134042014-11-06 Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis Abbasi, M. Jahanshahloo, G. R. Rostamy-Malkhlifeh, M. Hosseinzadeh Lotfi, F. ScientificWorldJournal Research Article This paper deals with evaluating congestion in free disposal hull (FDH) models. There are several approaches in data envelopment analysis (DEA) literatures which discuss the theory and application of congestion. However, almost all of these approaches considered convex DEA technologies. So, in the case of nonconvex technologies, including FDH technology, this field is almost nil. This paper makes an attempt to fill in this void. To do so, this study provides a pairwise comparisons-based algorithm to evaluate congestion in FDH model. This algorithm identifies the sources of congestion and estimates its amounts. It is also capable of detecting the losses amounts of output due to congestion. The validity of the proposed model is demonstrated using some numerical and empirical examples. Hindawi Publishing Corporation 2014 2014-10-15 /pmc/articles/PMC4213404/ /pubmed/25379531 http://dx.doi.org/10.1155/2014/427673 Text en Copyright © 2014 M. Abbasi et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Abbasi, M.
Jahanshahloo, G. R.
Rostamy-Malkhlifeh, M.
Hosseinzadeh Lotfi, F.
Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis
title Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis
title_full Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis
title_fullStr Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis
title_full_unstemmed Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis
title_short Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis
title_sort estimation of congestion in free disposal hull models using data envelopment analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213404/
https://www.ncbi.nlm.nih.gov/pubmed/25379531
http://dx.doi.org/10.1155/2014/427673
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