Chance constrained programming approaches to congestion in stochastic data envelopment analysis

The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their “deterministic equivalents.” This lead...

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Bibliographic Details
Published in:European journal of operational research Vol. 155; no. 2; pp. 487 - 501
Main Authors: Cooper, William W., Deng, H., Huang, Zhimin, Li, Susan X.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.06.2004
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Chance constrained programming
Congestion
Data envelopment analysis
DEA (data envelopment analysis)
Inefficiency
Nonlinear programming
Studies
Inefficiency
Congestion
DEA (data envelopment analysis)
Chance constrained programming
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their “deterministic equivalents.” This leads to a class of non-linear problems. However, it is shown to be possible to avoid some of the need for dealing with these non-linear problems by identifying conditions under which they can be replaced by ordinary (deterministic) DEA models. Examples which illustrate possible uses of these approaches are also supplied in an Appendix A.
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ISSN:0377-2217
1872-6860
DOI:10.1016/S0377-2217(02)00901-3