Nicole Megow, TU München
10:00 - 10:30 Lehrprobe k-Median Problem
10:30 - 11:15 Resource Minimization under Uncertainty
Uncertainty in the input data is an omnipresent issue when solving real-world optimization problems: jobs may take more or less time than originally estimated, resources may become unavailable, material arrives late, weather conditions may cause severe delays, etc. Uncertain data is typically modeled through stochastic parameters or as online information that is incrementally revealed. In this talk, I will discuss different models and solution methods for optimization under uncertainty. As a main example I show recent results on an online machine minimization problem, and I will talk about results and intriguing open questions on stochastic scheduling.