The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

As more uncontrollable renewable energy sources are present in the power generation
portfolio, the need of more detailed and reliable tools for the optimal operation of energy …

Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty,
dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A …

The topic of this book is multistage stochastic optimization. Multistage reflects the fact that an
optimal decision is an entire strategy or policy, which is executed during subsequent instants …

An important issue for solving multistage stochastic programs consists in the approximate
representation of the (multivariate) stochastic input process in the form of a scenario tree. In …

To make informed decisions in futures markets of electric energy, stochastic programming
models are commonly used. Such models treat stochastic processes via a set of scenarios …

The behaviour of stochastic programming problems is studied in case of the underlying
probability distribution being perturbed and approximated, respectively. Most of the …

While methods for optimization under uncertainty have been studied intensely over the past
decades, the explicit consideration of the interplay between uncertainty and time has gained …

This paper compares reinforcement learning (RL) with model predictive control (MPC) in a
unified framework and reports experimental results of their application to the synthesis of a …