The sample average approximation method for stochastic discrete optimization

Anton J. Kleywegt, Alexander Shapiro, Tito Homem-De-Mello

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

1260 Citas (Scopus)

Resumen

In this paper we study a Monte Carlo simulation-based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and the expected value function is approximated by the corresponding sample average function. The obtained sample average optimization problem is solved, and the procedure is repeated several times until a stopping criterion is satisfied. We discuss convergence rates, stopping rules, and computational complexity of this procedure and present a numerical example for the stochastic knapsack problem.

Idioma originalInglés
Páginas (desde-hasta)479-502
Número de páginas24
PublicaciónSIAM Journal on Optimization
Volumen12
N.º2
DOI
EstadoPublicada - 2002
Publicado de forma externa

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