| edoc-Server der Humboldt-Universität zu Berlin |
| Author(s): | Alexander Shapiro, Georgia Institute of Technology, Atlanta, Georgia | Title: | Stochastic programming by Monte Carlo simulation methods |
| Date of Acceptance: | 31.01.2000 |
| Submission Date: | 10.12.1999 |
| Series Title: |
Stochastic Programming E-Print Series (SPEPS) |
| Editors: | Julie L. Higle; Werner Römisch; Surrajeet Sen |
| Complete Preprint: | pdf (urn:nbn:de:kobv:11-10046191) |
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| Abstract (eng): | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| We consider in this paper stochastic programming problems which can be formulated as an optimization problem of an expected value function subject to deterministic constraints. We discuss a Monte Carlo simulation approach based on sample average approximations to a numerical solution of such problems. In particular, we give a survey of a statistical inference of the sample average estimators of the optimal value and optimal solutions of the true problem. We also discuss stopping rules and a validation analysis for such sample average approximation optimization procedures and give some illustration examples. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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