Robust optimization of specific scheduling problems

Abstract

In this thesis, we explore optimization problems whose parameters are uncertain. We speak about optimization under uncertainty. Specifically, we are interested in Robust Optimization (RO), a methodology used to tackle uncertain problems and which utilizes concepts like uncertainty sets and worst case scenarios. In the context of Supply Chain Management, we discuss how RO can be applied to scheduling problems under uncertainty. We mainly consider the flexible approach introduced in (Bertsimas & Sim, 2003) and study how it can be applied to three uncertain scheduling: the maximum lateness under uncertain due dates, the weighted sum of completion times under uncertain processing times and under uncertain weights.