Add to ORKGIn this work we consider uncertain optimization problems where no probability distribution is known. We introduce the approaches RecFeas and RecOpt to such a robust optimization problem, using a location theoretic point of view, and discuss both theoretical and algorithmic aspects. We then consider both continuous and discrete problem applications of robust optimization: Linear programs from the Netlib benchmark set, and the aperiodic timetabling problem on the continuous side; intermodal load planning, Steiner trees, periodic timetabling, and timetable information on the discrete side. Finally, we present the software library ROPI as a framework for robust optimization with support for most established mixed-integer programming solvers
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
Robust optimization is a methodology for dealing with uncertainty in optimization problems. In this ...
In this work we consider uncertain optimization problems where no probability distribution is known....
In dieser Arbeit betrachten wir unsichere Optimierungsprobleme, für die keine Wahrscheinlichkeitsve...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
This thesis discusses different methods for robust optimization problems that are convex in the unce...
In this paper, we introduce an approach for constructing uncertainty sets for robust optimization us...
In this paper we survey the primary research, both theoretical and applied, in the area of robust op...
Robust optimization (RO) has become a central framework to handle the uncertainty that arises in the...
Robust optimization (RO) has become a central framework to handle the uncertainty that arises in the...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
This thesis deals with taking uncertain data into account in optimization problems. Our focus is on ...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
Robust optimization is a methodology for dealing with uncertainty in optimization problems. In this ...
In this work we consider uncertain optimization problems where no probability distribution is known....
In dieser Arbeit betrachten wir unsichere Optimierungsprobleme, für die keine Wahrscheinlichkeitsve...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
This thesis discusses different methods for robust optimization problems that are convex in the unce...
In this paper, we introduce an approach for constructing uncertainty sets for robust optimization us...
In this paper we survey the primary research, both theoretical and applied, in the area of robust op...
Robust optimization (RO) has become a central framework to handle the uncertainty that arises in the...
Robust optimization (RO) has become a central framework to handle the uncertainty that arises in the...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
This thesis deals with taking uncertain data into account in optimization problems. Our focus is on ...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
We propose a novel approach for optimization under uncertainty. Our approach does not assume any par...
Robust optimization is a methodology for dealing with uncertainty in optimization problems. In this ...