Centralized resource allocation BCC models

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Centralized resource allocation BCC models. / Asmild, Mette; Paradi, Joseph C.; Pastor, Jesus T.

In: Omega, Vol. 37, No. 1, 02.2009, p. 40-49.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Asmild, M, Paradi, JC & Pastor, JT 2009, 'Centralized resource allocation BCC models', Omega, vol. 37, no. 1, pp. 40-49. https://doi.org/10.1016/j.omega.2006.07.006

APA

Asmild, M., Paradi, J. C., & Pastor, J. T. (2009). Centralized resource allocation BCC models. Omega, 37(1), 40-49. https://doi.org/10.1016/j.omega.2006.07.006

Vancouver

Asmild M, Paradi JC, Pastor JT. Centralized resource allocation BCC models. Omega. 2009 Feb;37(1):40-49. https://doi.org/10.1016/j.omega.2006.07.006

Author

Asmild, Mette ; Paradi, Joseph C. ; Pastor, Jesus T. / Centralized resource allocation BCC models. In: Omega. 2009 ; Vol. 37, No. 1. pp. 40-49.

Bibtex

@article{3f67903db60e44c9b3b71acbe044ad56,
title = "Centralized resource allocation BCC models",
abstract = "In two recent papers, Lozano and Villa [Centralized resource allocation using data envelopment analysis. Journal of Productivity Analysis 2004;22:143-61. [1]] and Lozano et al. [Centralized target setting for regional recycling operations using DEA. OMEGA 2004;32:101-10. [2]] introduce the concept of {"}centralized{"} data envelopment analysis (DEA) models, which aim at optimizing the combined resource consumption by all units in an organization rather than considering the consumption by each unit separately. This is particularly relevant for situations where some variables are controlled by a central authority (e.g. Head Office) rather than individual unit managers. In this paper we reconsider one of the centralized models proposed by the above-mentioned authors and suggest modifying it to only consider adjustments of previously inefficient units. We show how this new model formulation relate to a standard DEA model, namely as the analysis of the mean inefficient point. We also provide a procedure that can be used to generate alternative optimal solutions, enabling a decision maker to search through alternate solution possibilities in order to select the preferred one. We then extend the model to incorporate non-transferable as well as strictly non-discretionary variables and illustrate the models using an empirical example of a public service organization.",
keywords = "Allocation, DEA, Efficiency, LP, Mathematical programming, Operations Research/OR, Resource management",
author = "Mette Asmild and Paradi, {Joseph C.} and Pastor, {Jesus T.}",
year = "2009",
month = feb,
doi = "10.1016/j.omega.2006.07.006",
language = "English",
volume = "37",
pages = "40--49",
journal = "Omega: The International Journal of Management Science",
issn = "0305-0483",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Centralized resource allocation BCC models

AU - Asmild, Mette

AU - Paradi, Joseph C.

AU - Pastor, Jesus T.

PY - 2009/2

Y1 - 2009/2

N2 - In two recent papers, Lozano and Villa [Centralized resource allocation using data envelopment analysis. Journal of Productivity Analysis 2004;22:143-61. [1]] and Lozano et al. [Centralized target setting for regional recycling operations using DEA. OMEGA 2004;32:101-10. [2]] introduce the concept of "centralized" data envelopment analysis (DEA) models, which aim at optimizing the combined resource consumption by all units in an organization rather than considering the consumption by each unit separately. This is particularly relevant for situations where some variables are controlled by a central authority (e.g. Head Office) rather than individual unit managers. In this paper we reconsider one of the centralized models proposed by the above-mentioned authors and suggest modifying it to only consider adjustments of previously inefficient units. We show how this new model formulation relate to a standard DEA model, namely as the analysis of the mean inefficient point. We also provide a procedure that can be used to generate alternative optimal solutions, enabling a decision maker to search through alternate solution possibilities in order to select the preferred one. We then extend the model to incorporate non-transferable as well as strictly non-discretionary variables and illustrate the models using an empirical example of a public service organization.

AB - In two recent papers, Lozano and Villa [Centralized resource allocation using data envelopment analysis. Journal of Productivity Analysis 2004;22:143-61. [1]] and Lozano et al. [Centralized target setting for regional recycling operations using DEA. OMEGA 2004;32:101-10. [2]] introduce the concept of "centralized" data envelopment analysis (DEA) models, which aim at optimizing the combined resource consumption by all units in an organization rather than considering the consumption by each unit separately. This is particularly relevant for situations where some variables are controlled by a central authority (e.g. Head Office) rather than individual unit managers. In this paper we reconsider one of the centralized models proposed by the above-mentioned authors and suggest modifying it to only consider adjustments of previously inefficient units. We show how this new model formulation relate to a standard DEA model, namely as the analysis of the mean inefficient point. We also provide a procedure that can be used to generate alternative optimal solutions, enabling a decision maker to search through alternate solution possibilities in order to select the preferred one. We then extend the model to incorporate non-transferable as well as strictly non-discretionary variables and illustrate the models using an empirical example of a public service organization.

KW - Allocation

KW - DEA

KW - Efficiency

KW - LP

KW - Mathematical programming

KW - Operations Research/OR

KW - Resource management

U2 - 10.1016/j.omega.2006.07.006

DO - 10.1016/j.omega.2006.07.006

M3 - Journal article

AN - SCOPUS:44849130397

VL - 37

SP - 40

EP - 49

JO - Omega: The International Journal of Management Science

JF - Omega: The International Journal of Management Science

SN - 0305-0483

IS - 1

ER -

ID: 227787348