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

摘要

A genetic algorithm (GA) approach is developed to solve the P-model of chance constrained data envelopment analysis (CCDEA) problems, which include the concept of satisficing. Problems comprise cases in which inputs and outputs or only outputs are stochastic. 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. (101 refs.)