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Publications and Conferences

Our dissemination activities

What and Where We Published

Journal Articles

18

Proceedings

21

Book Chapters

2

Talks at Conferences

32

In Review (2)

Accepted (7)

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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)

  • Werth, B.; Karder, J.; Heckmann, M.; Wagner, S.; Affenzeller, M.; 2024. Applying Learning and Self-Adaptation to Dynamic Scheduling. Applied Sciences. 2024; 14, 49.

  • Fleck, P.; Werth, B.; Affenzeller, M.; 2024. Population Dynamics in Genetic Programming for Dynamic Symbolic Regression. Applied Sciences. 2024; 14(2):596.

  • 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.

  • 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

  • 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

  • Neuhauser, P.; Wagner, S.; 2024. Evolutionary Hyperparameter Tuning in ML.NET. Proceedings of the 36th European Modeling and Simulation Symposium EMSS2024

  • 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

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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

  • 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

  • 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.

  • Braune, R.; 2022. Packing-based branch-and-bound for discrete malleable task scheduling. Journal of Scheduling 25, 675-704.

  • Grabenschweiger, J.; Dörner, K.F.; Hartl, R.F.; 2022. The Multi-Period Location Routing Problem with Locker Boxes. Logistics Research 15(1), 1-25.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Roljic, B.; Raggl, S.; Dörner, K.F. 2021. Stacking and transporting steel slabs using high-capacity vehicles. Procedia Computer Science, 180, pp. 843-851.

  • 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.

  • 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.

The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Christian Doppler Research Association is gratefully acknowledged.

Federal Ministry for Digital and Economic Affairs
Christian Doppler Research Association

Research conducted within the JRC adaptOp is collaborative work of the University of Applied Sciences Upper Austria and the University of Vienna.

University of Applied Sciences Upper Austria
University of Vienna

JRC adaptOp is part of the HEAL research group at the Hagenberg Campus of the University of Applied Sciences Upper Austria.

HEAL Research Group