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- W288059780 abstract "Solving a multi-stage stochastic program with a large number of scenarios and a moderate-tolarge number of stages can be computationally challenging. We develop two Monte Carlo-based methods that exploit special structures to generate feasible policies. To establish the quality of a given policy, we employ a Monte Carlo-based lower bound (for minimization problems) and use it to construct a confidence interval on the policy’s optimality gap. The confidence interval can be formed in a number of ways depending on how the expected solution value of the policy is estimated and combined with the lower-bound estimator. Computational results suggest that a confidence interval formed by a tree-based gap estimator may be an effective method for assessing policy quality. Variance reduction is achieved by using common random numbers in the gap estimator." @default.
- W288059780 created "2016-06-24" @default.
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- W288059780 date "2004-05-17" @default.
- W288059780 modified "2023-09-23" @default.
- W288059780 title "Assessing policy quality in multi-stage stochastic programming" @default.
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- W288059780 doi "https://doi.org/10.18452/2951" @default.
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