Chance constrained programming approaches to congestion in stochastic data envelopment analysis

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. © 2003 Elsevier B.V. All rights reserved.