Two Stage Stochastic

Two-stage stochastic programming addresses optimization problems where decisions must be made in two stages, with uncertainty resolved between them. Current research focuses on improving the efficiency and scalability of solving these problems, particularly for non-convex objectives and large-scale applications, employing techniques like Bayesian optimization, reinforcement learning, and neural network approximations of the expected value function. These advancements are impacting diverse fields, from energy management (e.g., EV charging station control and wind farm bidding) to logistics and infrastructure planning, by enabling more robust and data-driven decision-making under uncertainty. The integration of machine learning methods is a key trend, offering faster and potentially higher-quality solutions compared to traditional approaches.