Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F15%3A86096347" target="_blank" >RIV/61989100:27510/15:86096347 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11227-015-1387-y" target="_blank" >http://dx.doi.org/10.1007/s11227-015-1387-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11227-015-1387-y" target="_blank" >10.1007/s11227-015-1387-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach

Popis výsledku v původním jazyce

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Název v anglickém jazyce

Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach

Popis výsledku anglicky

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Supercomputing

ISSN

0920-8542

e-ISSN

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Svazek periodika

71

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

2397-2411

Kód UT WoS článku

000357345600004

EID výsledku v databázi Scopus

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