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 …

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 …

Uncertainties in renewable energy sources and load demand have become a consequential
issue which has led to a significant effect on the microgrid operation. In this paper, a novel …

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 develop a unified framework for studying constrained robust optimal control
problems with adjustable uncertainty sets. In contrast to standard constrained robust optimal …

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

We demonstrate how adjustable robust optimization (ARO) problems with fixed recourse can
be cast as static robust optimization problems via Fourier–Motzkin elimination (FME) …

Multistage robust optimization problems, where the decision maker can dynamically react to
consecutively observed realizations of the uncertain problem parameters, pose formidable …

Mathematical optimization problems including a time dimension abound. For example,
logistics, process optimization and production planning tasks must often be optimized for a …

Problem definition: In each period of a planning horizon, an online retailer decides how
much to replenish each product and how to allocate its inventory to fulfillment centers (FCs) …