Risk assessment and management was established as a scientific field some 30–40 years
ago. Principles and methods were developed for how to conceptualise, assess and manage …

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

Distributionally robust optimization is a paradigm for decision making under uncertainty
where the uncertain problem data are governed by a probability distribution that is itself …

The last decade witnessed an explosion in the availability of data for operations research
applications. Motivated by this growing availability, we propose a novel schema for utilizing …

This paper provides an overview of developments in robust optimization since 2007. It seeks
to give a representative picture of the research topics most explored in recent years …

We develop tractable semidefinite programming based approximations for distributionally
robust individual and joint chance constraints, assuming that only the first-and second-order …

In this paper we survey the primary research, both theoretical and applied, in the area of
robust optimization (RO). Our focus is on the computational attractiveness of RO …

This paper considers the worst-case Conditional Value-at-Risk (CVaR) in the situation
where only partial information on the underlying probability distribution is available. The …

This book provides a comprehensive demonstration of risk analysis as a distinct science
covering risk understanding, assessment, perception, communication, management …

In this paper, we propose a methodology for constructing uncertainty sets within the
framework of robust optimization for linear optimization problems with uncertain parameters …