Timo Gersing
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Minimum‐Peak‐Cost Flows Over Time Open
Peak cost is a novel objective for flows over time that describes the amount of workforce necessary to run a system. We focus on minimizing peak costs in the context of maximum temporally repeated flows and formulate the corresponding MPC‐…
A Faster Parametric Search for the Integral Quickest Transshipment Problem Open
Algorithms for computing fractional solutions to the quickest transshipment problem have been significantly improved since Hoppe and Tardos first solved the problem in strongly polynomial time. For integral solutions, runtime improvements …
Recycling valid inequalities for robust combinatorial optimization with budgeted uncertainty Open
Robust combinatorial optimization with budgeted uncertainty is one of the most popular approaches for integrating uncertainty into optimization problems. The existence of a compact reformulation for (mixed-integer) linear programs and posi…
Computational Results: Algorithms for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide computational results for robust combinatorial optimization problems with budgeted uncertainty described in the thesis "Algorithms for Robust Combinatorial Optimization with Budgeted Uncertainty and Fair Planning of the Out-of-H…
Computational Results: Algorithms for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide computational results for robust combinatorial optimization problems with budgeted uncertainty described in the thesis "Algorithms for Robust Combinatorial Optimization with Budgeted Uncertainty and Fair Planning of the Out-of-H…
A branch and bound algorithm for robust binary optimization with budget uncertainty Open
Since its introduction in the early 2000s, robust optimization with budget uncertainty has received a lot of attention. This is due to the intuitive construction of the uncertainty sets and the existence of a compact robust reformulation f…
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function. The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the ro…
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function. The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the ro…
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function. The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the ro…
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide test instances for robust combinatorial optimization under budget uncertainty that have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", p…
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function. The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the ro…
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide a set of instances for robust combinatorial optimization under budget uncertainty that have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty…
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty Open
We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function. The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the ro…
Decision-Support Systems For Ambulatory Care, Including Pandemic Requirements: Using Mathematically Optimized Solutions Open
Background: The healthcare sector poses many strategic, tactic and operational planning questions. Due to the historically grown structures, planning is often locally confined and much optimization potential is foregone. Methods: We implem…