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Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis

The traditional data envelopment analysis (DEA) literatures generally concentrated on the efficiency evaluation of single decision making unit (DMU). However, in many practical problems, the decision makers are required to choose a number of DMUs instead of a single one from the DMUs set. Therefore,...

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Detalles Bibliográficos
Autores principales: Qi, Xiao-guang, Guo, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4119746/
https://www.ncbi.nlm.nih.gov/pubmed/25114971
http://dx.doi.org/10.1155/2014/789053
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author Qi, Xiao-guang
Guo, Bo
author_facet Qi, Xiao-guang
Guo, Bo
author_sort Qi, Xiao-guang
collection PubMed
description The traditional data envelopment analysis (DEA) literatures generally concentrated on the efficiency evaluation of single decision making unit (DMU). However, in many practical problems, the decision makers are required to choose a number of DMUs instead of a single one from the DMUs set. Therefore, it is necessary to study the combinatorial efficiency evaluation problem which can be illustrated as a knapsack problem naturally. It is indicated that the basic model proposed by Cook and Green may have some drawbacks and a modified model, which is combined with the super efficiency model, is proposed in this paper. What is more, our proposed model is more persuasive to the decision makers because it is able to provide a unique best combination of DMUs. An adapted local search algorithm is developed as a solver of this problem. Finally, numerical examples are provided to examine the validity of our proposed model and the adapted local search algorithm.
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spelling pubmed-41197462014-08-11 Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis Qi, Xiao-guang Guo, Bo ScientificWorldJournal Research Article The traditional data envelopment analysis (DEA) literatures generally concentrated on the efficiency evaluation of single decision making unit (DMU). However, in many practical problems, the decision makers are required to choose a number of DMUs instead of a single one from the DMUs set. Therefore, it is necessary to study the combinatorial efficiency evaluation problem which can be illustrated as a knapsack problem naturally. It is indicated that the basic model proposed by Cook and Green may have some drawbacks and a modified model, which is combined with the super efficiency model, is proposed in this paper. What is more, our proposed model is more persuasive to the decision makers because it is able to provide a unique best combination of DMUs. An adapted local search algorithm is developed as a solver of this problem. Finally, numerical examples are provided to examine the validity of our proposed model and the adapted local search algorithm. Hindawi Publishing Corporation 2014 2014-07-09 /pmc/articles/PMC4119746/ /pubmed/25114971 http://dx.doi.org/10.1155/2014/789053 Text en Copyright © 2014 X.-g. Qi and B. Guo. 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
Qi, Xiao-guang
Guo, Bo
Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis
title Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis
title_full Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis
title_fullStr Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis
title_full_unstemmed Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis
title_short Combinatorial Efficiency Evaluation: The Knapsack Problem in Data Envelopment Analysis
title_sort combinatorial efficiency evaluation: the knapsack problem in data envelopment analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4119746/
https://www.ncbi.nlm.nih.gov/pubmed/25114971
http://dx.doi.org/10.1155/2014/789053
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