Practical benchmarking in DEA using artificial DMUs

Abstract Data envelopment analysis (DEA) is one of the most efficient tools for efficiency measurement which can be employed as a benchmarking method with multiple inputs and outputs. However, DEA does not provide any suggestions for improving efficient units, nor does it provide any benchmark or re...

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Main Authors: Hosein Didehkhani, Farhad Hosseinzadeh Lotfi, Soheil Sadi-Nezhad
Format: Article
Language:English
Published: Islamic Azad University 2018-07-01
Series:Journal of Industrial Engineering International
Subjects:
Online Access:http://link.springer.com/article/10.1007/s40092-018-0281-7
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spelling doaj-5f2c5dee8bc2491fab0de69adb4adacb2021-02-02T06:06:54ZengIslamic Azad UniversityJournal of Industrial Engineering International1735-57022251-712X2018-07-0115229330110.1007/s40092-018-0281-7Practical benchmarking in DEA using artificial DMUsHosein Didehkhani0Farhad Hosseinzadeh Lotfi1Soheil Sadi-Nezhad2Department of Industrial Engineering, Science and Research Branch, Islamic Azad UniversityDepartment of Mathematics, Science and Research Branch, Islamic Azad UniversityDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad UniversityAbstract Data envelopment analysis (DEA) is one of the most efficient tools for efficiency measurement which can be employed as a benchmarking method with multiple inputs and outputs. However, DEA does not provide any suggestions for improving efficient units, nor does it provide any benchmark or reference point for these efficient units. Impracticability of these benchmarks under environmental conditions is another challenge of benchmarking by DEA. The current study attempts to extend basic models for benchmarking of efficient units under practical conditions. To this end, we construct the practical production possibility set (PPPS) by employing the concept of artificial decision-making units and adding these decision-making units to the production possibility set (PPS) such that these artificial units satisfy all environmental constraints. Then, the theorems related to PPPS and their proofs are provided. Moreover, as a secondary result of this study, efficient units can be ranked according to their practical efficiency scores.http://link.springer.com/article/10.1007/s40092-018-0281-7Artificial DMUBenchmarkingPractical production possibility set
collection DOAJ
language English
format Article
sources DOAJ
author Hosein Didehkhani
Farhad Hosseinzadeh Lotfi
Soheil Sadi-Nezhad
spellingShingle Hosein Didehkhani
Farhad Hosseinzadeh Lotfi
Soheil Sadi-Nezhad
Practical benchmarking in DEA using artificial DMUs
Journal of Industrial Engineering International
Artificial DMU
Benchmarking
Practical production possibility set
author_facet Hosein Didehkhani
Farhad Hosseinzadeh Lotfi
Soheil Sadi-Nezhad
author_sort Hosein Didehkhani
title Practical benchmarking in DEA using artificial DMUs
title_short Practical benchmarking in DEA using artificial DMUs
title_full Practical benchmarking in DEA using artificial DMUs
title_fullStr Practical benchmarking in DEA using artificial DMUs
title_full_unstemmed Practical benchmarking in DEA using artificial DMUs
title_sort practical benchmarking in dea using artificial dmus
publisher Islamic Azad University
series Journal of Industrial Engineering International
issn 1735-5702
2251-712X
publishDate 2018-07-01
description Abstract Data envelopment analysis (DEA) is one of the most efficient tools for efficiency measurement which can be employed as a benchmarking method with multiple inputs and outputs. However, DEA does not provide any suggestions for improving efficient units, nor does it provide any benchmark or reference point for these efficient units. Impracticability of these benchmarks under environmental conditions is another challenge of benchmarking by DEA. The current study attempts to extend basic models for benchmarking of efficient units under practical conditions. To this end, we construct the practical production possibility set (PPPS) by employing the concept of artificial decision-making units and adding these decision-making units to the production possibility set (PPS) such that these artificial units satisfy all environmental constraints. Then, the theorems related to PPPS and their proofs are provided. Moreover, as a secondary result of this study, efficient units can be ranked according to their practical efficiency scores.
topic Artificial DMU
Benchmarking
Practical production possibility set
url http://link.springer.com/article/10.1007/s40092-018-0281-7
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AT farhadhosseinzadehlotfi practicalbenchmarkingindeausingartificialdmus
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