Multistage Stochastic

Multistage stochastic optimization tackles the challenge of making sequential decisions under uncertainty, aiming to find optimal strategies that account for evolving information and probabilistic future outcomes. Current research focuses on improving the computational efficiency of existing algorithms like stochastic dual dynamic programming (SDDP) through novel approaches such as incorporating deep learning architectures (e.g., transformers, recurrent neural networks) and kernel methods, or by developing alternative frameworks like decision-focused forecasting. These advancements are crucial for addressing complex real-world problems across diverse fields, including energy systems, finance, and supply chain management, where computationally tractable solutions to large-scale multistage stochastic problems are essential for effective decision-making.