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 …

Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …

This is a substantial revision of the previous edition with added new material. The
presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk …

Structure of this book and how to read it This book is divided into three parts: Part I
Fundamentals, Part II Validation of nominations, Part III Verification of booked capacities …

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 …

Stochastic optimization problems often involve data distributions that change in reaction to
the decision variables. This is the case, for example, when members of the population …

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 …

In this chapter we begin with a brief explanation of linear programming (LP) since integer
programming (IP) is usually regarded as an extension of LP. Also most practical methods of …

Abstract The Unit Commitment problem in energy management aims at finding the optimal
productions schedule of a set of generation units while meeting various system-wide …