Annals of Operations Research

, Volume 66, Issue 4, pp 279–295

Chapter 13 Satisficing DEA models under chance constraints

Authors

  • W. W. Cooper
    • Graduate School of BusinessThe University of Texas at Austin
  • Zhimin Huang
    • Schools of Business and BankingAdelphi University, Garden City
  • Susan X. Li
    • Schools of Business and BankingAdelphi University, Garden City
Part IV Statistical And Stochastic Characterizations

DOI: 10.1007/BF02187302

Cite this article as:
Cooper, W.W., Huang, Z. & Li, S.X. Ann Oper Res (1996) 66: 279. doi:10.1007/BF02187302

Abstract

DEA (Data Envelopment Analysis) models and concepts are formulated here in terms of the “P-Models” of Chance Constrained Programming, which are then modified to contact the “satisficing concepts” of H.A. Simon. Satisficing is thereby added as a third category to the efficiency/inefficiency dichotomies that have heretofore prevailed in DEA. Formulations include cases in which inputs and outputs are stochastic, as well as cases in which only the outputs are stochastic. Attention is also devoted to situations in which variations in inputs and outputs are related through a common random variable. Extensions include new developments in goal programming with deterministic equivalents for the corresponding satisficing models under chance constraints.

Keywords

Efficiencysatisficingdata envelopment analysisstochastic efficiency
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Copyright information

© J.C. Baltzer AG, Science Publishers 1996