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 …

Stochastic optimization is an umbrella term that includes over a dozen fragmented
communities, using a patchwork of sometimes overlapping notational systems with …

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 …

A complete and accessible introduction to the real-world applications of approximate
dynamic programming With the growing levels of sophistication in modern-day operations, it …

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 …

This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …

A bi-level, three-stage Stochastic Mathematical Program with Equilibrium Constraints
(SMPEC) is proposed for quantifying and optimizing travel time resilience in roadway …

We present a new distributionally robust optimization model called robust stochastic
optimization (RSO), which unifies both scenario-tree-based stochastic linear optimization …

This book is the first in the market to treat single-and multi-period risk measures (risk
functionals) in a thorough, comprehensive manner. It combines the treatment of properties of …