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.
CITATION STYLE
Kleywegt, A. J., Shapiro, A., & Homem-De-Mello, T. (2002). The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 12(2), 479–502. https://doi.org/10.1137/S1052623499363220
Mendeley helps you to discover research relevant for your work.