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 …

We investigate the data-driven newsvendor problem when one has n observations of p
features related to the demand as well as historical demand data. Rather than a two-step …

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 are concerned with the interaction and integration between demand forecasting and
inventory control, in the context of supply chain operations. The majority of the literature is …

We develop a modular and tractable framework for solving an adaptive distributionally
robust linear optimization problem, where we minimize the worst-case expected cost over an …

Electric vehicles (EVs) have been proposed as a key technology to help cut down the
massive greenhouse gas emissions from the transportation sector. Unfortunately, because …

In this paper we focus on a linear optimization problem with uncertainties, having
expectations in the objective and in the set of constraints. We present a modular framework …

Emerging collaborative consumption business models have shown promise in terms of both
generating business opportunities and enhancing the efficient use of resources. In the …

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