Static robust optimization (RO) is a methodology to solve mathematical optimization
problems with uncertain data. The objective of static RO is to find solutions that are immune …

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

Robust optimization is a young and active research field that has been mainly developed in
the last 15 years. Robust optimization is very useful for practice, since it is tailored to the …

We consider an integrated berth allocation and quay crane assignment and scheduling
problem where the arrival times of the vessels may be affected by uncertainty. The problem …

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 …

We present a new partition-and-bound method for multistage adaptive mixed-integer
optimization (AMIO) problems that extends previous work on finite adaptability. The …

In this paper we propose a methodology for constructing decision rules for integer and
continuous decision variables in multiperiod robust linear optimization problems. This type …

Testifying to more than 10 years of academic and practical developments, this tutorial
attempts to provide a succinct yet unified view of the robust multistage decision-making …

In recent years, decision rules have been established as the preferred solution method for
addressing computationally demanding, multistage adaptive optimization problems. Despite …

The efficacy of robust optimization spans a variety of settings with uncertainties bounded in
predetermined sets. In many applications, uncertainties are affected by decisions and …