10 citations · 42 across the 47 of their papers we have counts for
55 papers
The complexity landscape of robust (integer) linear programming
Michael Poss, Jannis Kurtz, Marc Goerigk +1
We study the computational complexity of the decision versions of three classic robust optimization problems: static robust optimization, two-stage (adjustable) robust optimization…
The Complexity Landscape of Two-Stage Robust Selection Problems with Budgeted Uncertainty
Marc Goerigk, Dorothee Henke, Lasse Wulf
A standard type of uncertainty set in robust optimization is budgeted uncertainty, where an interval of possible values for each parameter is given and the total deviation from the…
The robust selection problem with information discovery
Xiaoyu Chen, Marc Goerigk, Michael Poss
We explore a multiple-stage variant of the min-max robust selection problem with budgeted uncertainty that includes queries. First, one queries a subset of items and gets the exact…
A fast approximate column-and-constraint generation method for two-stage robust mixed-integer programs
Marc Goerigk, Dorothee Henke, Johannes Kager +2
This paper presents a new column-and-constraint generation method for two-stage robust mixed-integer programs with finite uncertainty sets. Our method combines and extends speed-up…
Problem-Driven Scenario Reduction and Scenario Approximation for Robust Optimization
Jamie Fairbrother, Marc Goerigk, Mohammad Khosravi
In robust optimization, we would like to find a solution that is immunized against all scenarios that are modeled in an uncertainty set. Which scenarios to include in such a set is…
An extension of Ordered Weighted Averaging over intervals with application to optimization under risk
Werner Baak, Marc Goerigk, Adam Kasperski +1
The Ordered Weighted Averaging (OWA) operator is a traditional and commonly used criterion for aggregating discrete values of uncertain quantities. In this paper, it is shown that…
Robust Min-Max (Regret) Optimization using Ordered Weighted Averaging
Werner Baak, Marc Goerigk, Adam Kasperski +1
In decision-making under uncertainty, several criteria have been studied to aggregate the performance of a solution over multiple possible scenarios. This paper introduces a novel…
A Framework for Data-Driven Explainability in Mathematical Optimization
Kevin-Martin Aigner, Marc Goerigk, Michael Hartisch +2
Advancements in mathematical programming have made it possible to efficiently tackle large-scale real-world problems that were deemed intractable just a few decades ago. However, p…
Robust Combinatorial Optimization Problems Under Budgeted Interdiction Uncertainty
Marc Goerigk, Mohammad Khosravi
In robust combinatorial optimization, we would like to find a solution that performs well under all realizations of an uncertainty set of possible parameter values. How we model th…
Robust optimization with belief functions
Marc Goerigk, Romain Guillaume, Adam Kasperski +1
In this paper, an optimization problem with uncertain objective function coefficients is considered. The uncertainty is specified by providing a discrete scenario set, containing p…
On the Complexity of Robust Multi-Stage Problems in the Polynomial Hierarchy
Marc Goerigk, Stefan Lendl, Lasse Wulf
We study the computational complexity of multi-stage robust optimization problems. Such problems are formulated with alternating min/max quantifiers and therefore naturally fall in…
Optimal Scenario Reduction for One- and Two-Stage Robust Optimization
Marc Goerigk, Mohammad Khosravi
Robust optimization typically follows a worst-case perspective, where a single scenario may determine the objective value of a given solution. Accordingly, it is a challenging task…
A Framework for Inherently Interpretable Optimization Models
Marc Goerigk, Michael Hartisch
With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world…
A Preference Elicitation Approach for the Ordered Weighted Averaging Criterion using Solution Choice Observations
Werner Baak, Marc Goerigk, Michael Hartisch
Decisions under uncertainty or with multiple objectives usually require the decision maker to formulate a preference regarding risks or trade-offs. If this preference is known, the…
A faster exact method for solving the robust multi-mode resource-constrained project scheduling problem
Matthew Bold, Marc Goerigk
This paper presents a mixed-integer linear programming formulation for the multi-mode resource-constrained project scheduling problem with uncertain activity durations. We consider…
Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization
Marc Goerigk, Jannis Kurtz
We study iterative methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. We propose a machine-learning-based heuristic to determine starting…
Benchmarking Problems for Robust Discrete Optimization
Marc Goerigk, Mohammad Khosravi
Robust discrete optimization is a highly active field of research where a plenitude of combinations between decision criteria, uncertainty sets and underlying nominal problems are…
Combinatorial Optimization Problems with Balanced Regret
Marc Goerigk, Michael Hartisch
For decision making under uncertainty, min-max regret has been established as a popular methodology to find robust solutions. In this approach, we compare the performance of our so…
On the Recoverable Traveling Salesman Problem
Marc Goerigk, Stefan Lendl, Lasse Wulf
In this paper we consider the Recoverable Traveling Salesman Problem (TSP). Here the task is to find two tours simultaneously, such that the intersection between the tours is at le…
Investigating the Recoverable Robust Single Machine Scheduling Problem Under Interval Uncertainty
Matthew Bold, Marc Goerigk
We investigate the recoverable robust single machine scheduling problem under interval uncertainty. In this setting, jobs have first-stage processing times p and second-stage proce…
Two-Stage Robust Optimization Problems with Two-Stage Uncertainty
Marc Goerigk, Stefan Lendl, Lasse Wulf
We consider two-stage robust optimization problems, which can be seen as games between a decision maker and an adversary. After the decision maker fixes part of the solution, the a…
Data-Driven Robust Optimization using Unsupervised Deep Learning
Marc Goerigk, Jannis Kurtz
Robust optimization has been established as a leading methodology to approach decision problems under uncertainty. To derive a robust optimization model, a central ingredient is to…
Recoverable Robust Single Machine Scheduling with Polyhedral Uncertainty
Matthew Bold, Marc Goerigk
This paper considers a recoverable robust single-machine scheduling problem under polyhedral uncertainty with the objective of minimising the total flow time. In this setting, a de…
Robust Optimization Approaches for Routing and Scheduling of Multi-Skilled Teams under Uncertain Job Skill Requirements
Yulia Anoshkina, Marc Goerigk, Frank Meisel
We consider a combined problem of teaming and scheduling of multi-skilled employees that have to perform jobs with uncertain qualification requirements. We propose two modeling app…