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Chapter 13 Satisficing DEA models under chance constraints
Authors:W W Cooper  Zhimin Huang  Susan X Li
Institution:(1) Graduate School of Business, The University of Texas at Austin, 78712-1174 Austin, TX, USA;(2) Schools of Business and Banking, Adelphi University, Garden City, 11530 Long Island, NY, USA
Abstract:DEA (Data Envelopment Analysis) models and concepts are formulated here in terms of the ldquoP-Modelsrdquo of Chance Constrained Programming, which are then modified to contact the ldquosatisficing conceptsrdquo 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:Efficiency  satisficing  data envelopment analysis  stochastic efficiency
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