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Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems
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DOI：10.1016/j.omega.2010.09.002.png)
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Genetic algorithm (GA) approach is developed for solving the P-model of chance constrained data envelopment analysis (CCDEA) problems, which include the concept of Satisficing. Problems here include cases in which inputs and outputs are stochastic, as well as cases in which only the outputs are stochastic. The basic solution technique for the above has so far been deriving deterministic equivalents, which is difficult for all stochastic parameters as there are no compact methods available. In the proposed approach, the stochastic objective function and chance constraints are directly used within the genetic process. The feasibility of chance constraints are checked by stochastic simulation techniques. A case of Indian banking sector has been presented to illustrate the above approach. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Data envelopment analysis
Satisficing
Stochastic efficiency
Stochastic simulation
Genetic algorithm
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