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Chapter 13 Satisficing DEA models under chance constraints

  • Part IV Statistical And Stochastic Characterizations
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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.

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Authors and Affiliations

  1. Graduate School of Business, The University of Texas at Austin, 78712-1174, Austin, TX, USA

    W. W. Cooper

  2. Schools of Business and Banking, Adelphi University, Garden City, 11530, Long Island, NY, USA

    Zhimin Huang & Susan X. Li

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  1. W. W. Cooper
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  2. Zhimin Huang
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  3. Susan X. Li
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Cooper, W.W., Huang, Z. & Li, S.X. Chapter 13 Satisficing DEA models under chance constraints. Ann Oper Res 66, 279–295 (1996). https://doi.org/10.1007/BF02187302

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