stochastic programming

What are Some Common Techniques Used?

Some common techniques in stochastic programming include:
Scenario Analysis: Creating and analyzing different possible future states.
Monte Carlo Simulation: Using random sampling to understand the impact of uncertainty.
Two-Stage Stochastic Programming: Optimizing decisions in two stages, considering initial decisions and subsequent adjustments.
Stochastic Dynamic Programming: Extending the optimization over multiple stages with decision rules.
Robust Optimization: Focusing on solutions that are feasible under all scenarios.

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