Programme

There are many deep papers on the mathematics and algorithmics of stochastic programming. But why should we, as operations research people, care? The world is stochastic for sure, but does that imply that we need stochastic models to get good decisions? And if we embark on a genuine application, where real money is involved, what are the modeling questions we need to pose? What are the steps we need to take before we arrive at mathematical and algorithmic challenges?

Interval Linear Programming (LP) provides theoretical foundations and methods for handling LP problems with interval coefficients. While such intervals often represent uncertain input data, we focus on fundamentally different applications. Specifically, we demonstrate how interval LP techniques can be used to address numerical issues in classical real LP, to construct relaxations in global optimization, and to conduct a more comprehensive sensitivity analysis that may involve all coefficients simultaneously.