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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| Veröffentlicht in: | European journal of operational research Jg. 155; H. 2; S. 487 - 501 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier B.V
01.06.2004
Elsevier Elsevier Sequoia S.A |
| Schriftenreihe: | European Journal of Operational Research |
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| ISSN: | 0377-2217, 1872-6860 |
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| Abstract | 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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| AbstractList | 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. 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. |
| Author | Li, Susan X. Cooper, William W. Huang, Zhimin Deng, H. |
| Author_xml | – sequence: 1 givenname: William W. surname: Cooper fullname: Cooper, William W. email: cooperw@mail.utexas.edu organization: The Red McCombs School of Business, University of Texas at Austin, Austin, TX 78712-1174, USA – sequence: 2 givenname: H. surname: Deng fullname: Deng, H. organization: The Red McCombs School of Business, University of Texas at Austin, Austin, TX 78712-1174, USA – sequence: 3 givenname: Zhimin surname: Huang fullname: Huang, Zhimin organization: School of Business, Adelphi University, Garden City, NY 11530, USA – sequence: 4 givenname: Susan X. surname: Li fullname: Li, Susan X. organization: School of Business, Adelphi University, Garden City, NY 11530, USA |
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| Keywords | Inefficiency Congestion DEA (data envelopment analysis) Chance constrained programming |
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| SubjectTerms | Chance constrained programming Congestion Data envelopment analysis DEA (data envelopment analysis) Inefficiency Nonlinear programming Studies |
| Title | Chance constrained programming approaches to congestion in stochastic data envelopment analysis |
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