What and Where We Published
Journal Articles
18
Proceedings
21
Book Chapters
2
Talks at Conferences
32
In Review (2)
Accepted (7)
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Braune, R.; 2024. Learning-based Algorithm Selection for a Multiprocessor Scheduling Problem. 19th International Conference on Computer Aided Systems Theory - EUROCAST 2024 Las Palmas de Gran Canaria, Spain.
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Braune, R.; Raunig, M.; 2024. Predicting the Processing Effort for Block Relocation Problems. 19th International Conference on Computer Aided Systems Theory - EUROCAST 2024 Las Palmas de Gran Canaria, Spain.
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Fleck, P.; Neuhauser, P.; Leitner, S.; Wagner, S.; 2024. Re-evaluation in Dynamic Tree-Search with Backtracking from Known Solutions. 19th International Conference on Computer Aided Systems Theory - EUROCAST 2024 Las Palmas de Gran Canaria, Spain.
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Heckmann, M.; Werth, B.; Karder, J.; Wagner, S.; 2024. Online Machine Learning for the Estimation of Process Times in Dynamic Scheduling. 19th International Conference on Computer Aided Systems Theory - EUROCAST 2024 Las Palmas de Gran Canaria, Spain.
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Leitner, S.; Wagner, S.; Affenzeller, M.; 2024. Incrementally Solving the Dynamic Stacking Problem. 19th International Conference on Computer Aided Systems Theory - EUROCAST 2024 Las Palmas de Gran Canaria, Spain.
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Werth, B.; Karder, J.; Wagner, S.; Affenzeller, M.; 2024. Diversity Management in Evolutionary Dynamic Optimization. 19th International Conference on Computer Aided Systems Theory - EUROCAST 2024 Las Palmas de Gran Canaria, Spain.
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Neuhauser, P.; Fleck, P.; Leitner, S.; Wagner, S.; 2024. Machine Learning Update Strategies for Real-time Production Environments. 19th International Conference on Computer Aided Systems Theory - EUROCAST 2024 Las Palmas de Gran Canaria, Spain.
Published (32)
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Werth, B.; Karder, J.; Heckmann, M.; Wagner, S.; Affenzeller, M.; 2024. Applying Learning and Self-Adaptation to Dynamic Scheduling. Applied Sciences. 2024; 14, 49.
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Fleck, P.; Werth, B.; Affenzeller, M.; 2024. Population Dynamics in Genetic Programming for Dynamic Symbolic Regression. Applied Sciences. 2024; 14(2):596.
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Neuhauser, P.; Cihal, R.; Wagner, S.; Flößholzer, H.; 2024. Efficient Rollout of a Dynamic Optimization Algorithm. Procedia Computer Science Volume 232, 2024, Pages 77-86.
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Heckmann, M.; Werth, B.; Wagner, S.; 2024. Convergence Analysis of Genetic Algorithms on Dynamic Production Scheduling. Proceedings of the 36th European Modeling and Simulation Symposium EMSS2024
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Leitner, S.; Fleck, P.; Wagner, S.; Affenzeller, M.; 2024. Dynamic transport-lot assignment for the hot-storage area. Proceedings of the 36th European Modeling and Simulation Symposium EMSS2024
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Neuhauser, P.; Wagner, S.; 2024. Evolutionary Hyperparameter Tuning in ML.NET. Proceedings of the 36th European Modeling and Simulation Symposium EMSS2024
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Werth, B.; Karder, J.; Wagner, S.; Affenzeller, M.; 2024. Efficient Global Optimization for Dynamic Problems. Proceedings of the 36th European Modeling and Simulation Symposium EMSS2024
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Karder, J.; Leitner, S.; Werth, B.; Wagner, S.; 2024. DynStack Competition - Dynamic Stacking Optimization in Uncertain Environments. In Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion (GECCO '24). Association for Computing Machinery, pp. 7-8.
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Neuhauser, P.; Wagner, S.; 2023. Performance Comparison of Microsoft's AutoML API. Proceedings of the 35th European Modeling and Simulation Symposium EMSS2023, pp. 193-200.
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Neuhauser, P.; Karder, J.; Beham, A.; Werth, B.; Leitner, S.; Wagner, S.; 2023. Integriertes maschinelles Lernen zur echtzeitfähigen Produktionsoptimierung und Entscheidungsfindung in der Logistik. In T. Wakolbinger (Ed.), Jahrbuch der Logistikforschung: Innovative Anwendungen, Konzepte & Technologien (1 ed., Vol. 4, pp. 175-187). Trauner Verlag Linz.
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Werth, B.; Karder, J.; Beham, A.; Pitzer, E.; Wagner, S.; 2023. Walking through the Quadratic Assignment-Instance Space: Algorithm Performance and Landscape Measures. In Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (GECCO '23). Association for Computing Machinery, pp. 2108-2114.
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Karder, J.; Werth, B.; Beham, A.; Wagner, S.; Affenzeller, M.; 2023. Novel Benchmark Environment for Dynamic Factory Crane Scheduling. Procedia Computer Science Volume 217, 2023, Pages 1217-1224.
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Werth, B.; Karder, J.; Beham, A.; Altendorfer, K.; 2023. Simulation-based Optimization of Material Requirements Planning Parameters. Procedia Computer Science Volume 217, 2023, Pages 1117-1126.
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Werth, B.; Pitzer, E.; Karder, J.; Wagner, S.; Affenzeller, M.; 2022. Dynamic Vehicle Routing with Time-Linkage: From Problem States to Algorithm Performance. 18th International Conference on Computer Aided Systems Theory - EUROCAST 2022 Las Palmas de Gran Canaria, Spain.
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Beham, A.; Leitner, S.; Karder, J.; Werth, B.; Wagner, S.; 2022. DynStack - A Benchmarking Framework for Dynamic Optimization Problems in Warehouse Operations. In Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion (GECCO '22). Association for Computing Machinery
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Karder, J.; Werth, B.; Beham, A.; Wagner, S; Affenzeller, M.; 2022. Analysis and Handling of Dynamic Problem Changes in Open-Ended Optimization. Computer Aided Systems Theory – Eurocast 2022 - 18th International Conference
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Werth, B; Beham, A.; Karder, J; Wagner, S.; Affenzeller, M.; 2022. Fitness Landscape Analysis on Binary Dynamic Optimization Problems. Procedia Computer Science, 200, 1004-1013.
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Braune, R.; 2022. Packing-based branch-and-bound for discrete malleable task scheduling. Journal of Scheduling 25, 675-704.
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Grabenschweiger, J.; Dörner, K.F.; Hartl, R.F.; 2022. The Multi-Period Location Routing Problem with Locker Boxes. Logistics Research 15(1), 1-25.
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Braune, R.; Benda, F.; Dörner, K.F.; Hartl, R.F.; 2022. A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems. International Journal of Production Economics Vol. 243.
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Beham, A.; Raggl, S.; Karder, J.; Werth, B.; Wagner, S; 2022. Dynamic Warehouse Environments for Crane Stacking and Scheduling. Procedia Computer Science, 200, 1461-1470.
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Beham, A.; Raggl, S.; Hauder, V.A.; Karder, J.; Wagner, S.; Affenzeller, M; 2020. Performance, Quality, and Control in Steel Logistics 4.0. Procedia Manufacturing, pp. 429-433.
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Grabenschweiger, J.; Doerner, K.F.; Hartl, R.F.; Savelsbergh, M.W.P.; 2021. The vehicle routing problem with heterogeneous locker boxes. Central European Journal of Operations Research, 29, pp. 113–142.
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Beham, A.; Leitner, S.; Karder, J.; Werth, B.; Hauder, V.A.; Wagner, S.; 2021. Architekturen und Modelle für dynamische Logistikoptimierung. In T. Wakolbinger (Ed.), Jahrbuch der Logistikforschung: Innovative Anwendungen, Konzepte & Technologien (1 ed., Vol. 3, pp. 175-184). Trauner Verlag Linz.
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Hauder, V.A.; Beham, A.; Raggl, S.; Parragh, S.N.; Affenzeller, M. 2020. Resource-constrained multi-project scheduling with activity and time flexibility. Computers & Industrial Engineering, 150, 106857, p. 14.
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Hauder, V.A.; Beham, A.; Wagner, S.; Doerner, K.; Affenzeller, M. 2020. Dynamic online optimization in the context of smart manufacturing: an overview. Procedia Computer Science, 180, pp. 988-995.
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Karder, J.; Beham, A.; Werth, B.; Wagner, S.; Affenzeller, M. 2022. Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service Times. Procedia Computer Science, 200, pp. 1031-1040.
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Raggl, S.; Beham, A.; Wagner, S; Affenzeller, M. 2020. Solution Approaches for the Dynamic Stacking Problem. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20). Association for Computing Machinery, pp. 1652-1660.
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Raggl, S.; Beham, A.; Wagner, S; Affenzeller, M. 2020. Effects of Arrival Uncertainty on Solver Performance in Dynamic Stacking Problems. Proceedings of the 32nd European Modeling and Simulation Symposium EMSS2020, pp. 193-200.
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Roljic, B.; Raggl, S.; Dörner, K.F. 2021. Stacking and transporting steel slabs using high-capacity vehicles. Procedia Computer Science, 180, pp. 843-851.
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Werth, B.; Karder, J.; Beham, A.; Wagner, S. 2021. Dynamic landscape analysis for open-ended stacking. In Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion (GECCO '21). Association for Computing Machinery, pp. 1700-1707.
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Werth, B.; Karder, J.; Beham, A.; Wagner, S. 2020. Hyper-Parameter Handling for Gaussian Processes in Efficient Global Optimization. Proceedings of the 19th international conference on Modelling and Applied Simulation MAS2020, pp. 60-67.