From Data to Decisions: Multi-Stage Optimization under Uncertainty

21/11/2018 - 15:00 - 16:00

In real world problems, we often have parameters that are not exactly known due to measurement, implementation, prediction errors, and uncertainty. When solving optimization problems, we want to find a solution that will be feasible and work well in practice despite these uncertainties. Finding such a solution becomes harder when dealing with a multi-stage problem, in which we need to devise a strategy to make the best decision at each stage, without knowing the future realization of the uncertainty. These problems arise in real-world applications such as inventory control, energy systems, portfolio management and many others.

In this talk, we will review existing robust and adaptive optimization approaches for solving two-stage and multi-stage optimization problems with uncertain parameters. Specifically, we look at the case of multi-stage linear stochastic optimization problems where the only information given about the underlying distribution is in the form of data. We suggest a model called sample robust-optimization (SRO) in which we robustify the decisions against perturbation in the data points. We show that SRO results in both asymptotic performance guarantees and a desirable structure amiable to approximation. We demonstrate the performance of such approximation in numerical experiments.

Shimrit Shtern, Technion
BIU Engineering Building 1103, Room 329