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Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems

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  • Udhayakumar, A.
  • Charles, V.
  • Kumar, Mukesh

Abstract

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.

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Bibliographic Info

Article provided by Elsevier in its journal Omega.

Volume (Year): 39 (2011)
Issue (Month): 4 (August)
Pages: 387-397

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Handle: RePEc:eee:jomega:v:39:y:2011:i:4:p:387-397

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Keywords: Data envelopment analysis Satisficing Stochastic efficiency Stochastic simulation Genetic algorithm;

References

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Cited by:
  1. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2012. "Determining the optimal double-component assignment for a stochastic computer network," Omega, Elsevier, Elsevier, vol. 40(1), pages 120-130, January.
  2. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, Elsevier, vol. 44(C), pages 32-40.

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