10 citations · 42 across the 47 of their papers we have counts for
12 papers · 1 filter
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…
Multistage Robust Discrete Optimization via Quantified Integer Programming
Marc Goerigk, Michael Hartisch
Decision making needs to take an uncertain environment into account. Over the last decades, robust optimization has emerged as a preeminent method to produce solutions that are imm…
Robust Combinatorial Optimization with Locally Budgeted Uncertainty
Marc Goerigk, Stefan Lendl
Budgeted uncertainty sets have been established as a major influence on uncertainty modeling for robust optimization problems. A drawback of such sets is that the budget constraint…
Recoverable Robust Representatives Selection Problems with Discrete Budgeted Uncertainty
Marc Goerigk, Stefan Lendl, Lasse Wulf
Recoverable robust optimization is a multi-stage approach, where it is possible to adjust a first-stage solution after the uncertain cost scenario is revealed. We analyze this appr…
Combinatorial two-stage minmax regret problems under interval uncertainty
Marc Goerigk, Adam Kasperski, Pawel Zielinski
In this paper a class of combinatorial optimization problems is discussed. It is assumed that a feasible solution can be constructed in two stages. In the first stage the objective…
Automatic Generation of Algorithms for Black-Box Robust Optimisation Problems
Martin Hughes, Marc Goerigk, Trivikram Dokka
We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly t…
A compact reformulation of the two-stage robust resource-constrained project scheduling problem
Matthew Bold, Marc Goerigk
This paper considers the resource-constrained project scheduling problem with uncertain activity durations. We assume that activity durations lie in a budgeted uncertainty set, and…
An Efficient Approach to Distributionally Robust Network Capacity Planning
Trivikram Dokka, Francis Garuba, Marc Goerigk +1
In this paper, we consider a network capacity expansion problem in the context of telecommunication networks, where there is uncertainty associated with the expected traffic demand…
Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty
Martin Hughes, Marc Goerigk, Trivikram Dokka
We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution with…
A Comparison of Discrete and Polyhedral Uncertainty Sets for Robust Network Design
Francis Garuba, Marc Goerigk, Peter Jacko
We consider a network design and expansion problem, where we need to make a capacity investment now, such that uncertain future demand can be satisfied as closely as possible. To u…