Skip to main content

IEOR Seminar: By Anita Schöbel, University of Göttingen, Germany

Title: New concepts in robust optimization

Speaker: Anita Schöbel

Date and Time: 15 November 2018, 10.30 a.m.

Venue: IEOR Seminar Room (Second floor)

Abstract: Most real-world optimization problems contain parameters which are not known at the time a decision is to be made. In robust optimization one specifies the uncertainty in a scenario set and tries to hedge against the worst case.

Classical robust optimization aims at finding a solution which is best in the worst-case scenario. It is a well-studied concept (see [1]), but it is known to be very conservative: A robust solution comes with a high price in its nominal objective function value.

This motivated researchers to introduce less conservative robustness concepts in the last decade, see the survey [2]. In the main part of this talk, two of such less conservative robustness approaches will be introduced and discussed: Light robustness and a scenario-based approach to recovery robustness. While light robustness ensures a pre-defined nominal quality, recovery robustness allows to adapt a solution if the true scenario becomes known. It will be shown how algorithms for solving the deterministic problems can be adapted to find lightly robust and recovery robust solutions.

In the second part of the talk we go one step further: How to handle uncertain optimization problems in which more than one objective function is to be considered? This yields a robust multiobjective optimization problem, a class of problems only recently introduced and studied, see [3]. Some concepts on how to define and visualize robust Pareto solutions will be introduced and first approaches on how to compute them will be mentioned.

The concepts will be illustrated for timetabling in public transportation.

[1] A. Ben-Tal and L. El Ghaoui and A. Nemirovski, Robust Optimization, Princeton University Press, 2009

[2] M. Goerigk and A. Schöbel. Algorithm Engineering in Robust Optimization, in: Algorithm Engineering: Selected Results and Surveys, ed: L. Kliemann and P. Sanders, LNCS 9220, 245-279, 2016

[3] J. Ide and A. Schöbel. Robustness for uncertain multi-objective optimization: a survey and analysis of different concepts, OR Spectrum 38:1, 235-271, 2016

Bio: Anita Schöbel is Professor for optimization at the Institute for Numerical and Applied Mathematics at the University of Göttingen, Germany. She works in Continuous optimization in location theory and Discrete optimization in public transport applications, among other things.
 

News Category