Felix Winter
Univ.Ass. Dipl.-Ing. Dr.techn. / BSc
Role
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PostDoc Researcher
Databases and Artificial Intelligence, E192-02
Courses
2023W
- Bachelor Thesis / 184.691 / PR
- Project in Computer Science 1 / 192.021 / PR
- Project in Computer Science 2 / 192.022 / PR
Projects
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CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling
2017 – 2024 / Christian Doppler Research Association (CDG)
Publications: 136808 / 139849 / 138379 / 138399 / 138400 / 138528 / 138530 / 144345 / 142199 / 142211 / 142210 / 142193 / 141227 / 141228 / 143220 / 143222 / 24775 / 55565 / 55636 / 57470 / 57475 / 57476 / 57570 / 57572 / 57573 / 57872 / 57873 / 57874 / 57876 / 57878 / 57880 / 58293 / 58340 / 58583 / 58584 / 58585 / 58586 / 58590 / 58591 / 86907 / 87083
Publications
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Exact and meta-heuristic approaches for the production leveling problem
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Vass, J., Lackner, M.-L., Mrkvicka, C., Musliu, N., & Winter, F. (2022). Exact and meta-heuristic approaches for the production leveling problem. Journal of Scheduling, 25(3), 339–370. https://doi.org/10.1007/s10951-022-00721-1
Download: PDF (1.5 MB)
Project: ARTIS (2017–2024) -
An Investigation of Hyper-Heuristic Approaches for Teeth Scheduling
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Winter, F., & Musliu, N. (2022). An Investigation of Hyper-Heuristic Approaches for Teeth Scheduling. In MIC 2022: 14th Metaheuristics International Conference. 14th Metaheuristics International Conference (MIC 2022), Ortigia-Syracuse, Italy. Springer.
Project: ARTIS (2017–2024) -
Modeling and Solving the K-track Assignment Problem
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Preininger, J., Winter, F., & Musliu, N. (2022). Modeling and Solving the K-track Assignment Problem. In 14th Metaheuristics International Conference. MIC 2022 - 14th Metaheuristics International Conference, Ortigia-Syracuse, Italy. Springer. http://hdl.handle.net/20.500.12708/142199
Project: ARTIS (2017–2024) -
A Hybrid Approach for Paint Shop Scheduling in the Automotive Supply Industry
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Winter, F., & Musliu, N. (2022). A Hybrid Approach for Paint Shop Scheduling in the Automotive Supply Industry. In Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling (pp. 317–320). http://hdl.handle.net/20.500.12708/142193
Project: ARTIS (2017–2024) -
Solving the Production Leveling Problem with Order-Splitting and Resource Constraints
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Vass, J., Musliu, N., & Winter, F. (2022). Solving the Production Leveling Problem with Order-Splitting and Resource Constraints. In Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling (pp. 261–284). http://hdl.handle.net/20.500.12708/142211
Project: ARTIS (2017–2024) -
Solving an Industrial Oven Scheduling Problem with a Simulated Annealing Approach
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Lackner, M.-L., Musliu, N., & Winter, F. (2022). Solving an Industrial Oven Scheduling Problem with a Simulated Annealing Approach. In Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling (pp. 115–120). http://hdl.handle.net/20.500.12708/142210
Project: ARTIS (2017–2024) -
Automated Production Scheduling for Artificial Teeth Manufacturing
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Mrkvicka, C., Musliu, N., Preininger, J., & Winter, F. (2021). Automated Production Scheduling for Artificial Teeth Manufacturing. In Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, {ICAPS} 2021, Guangzhou, China (virtual), August 2-13, 2021}, (pp. 500–508). http://hdl.handle.net/20.500.12708/58583
Project: ARTIS (2017–2024) -
Automated scheduling for automotive supplier paint shops and teeth manufacturing
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Winter, F. (2021). Automated scheduling for automotive supplier paint shops and teeth manufacturing [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.100623
Download: PDF (1.7 MB) -
Exact and metaheuristic approaches for unrelated parallel machine scheduling
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Moser, M., Musliu, N., Schaerf, A., & Winter, F. (2021). Exact and metaheuristic approaches for unrelated parallel machine scheduling. Journal of Scheduling, 25(5), 507–534. https://doi.org/10.1007/s10951-021-00714-6
Project: ARTIS (2017–2024) -
A large neighborhood search approach for the paint shop scheduling problem
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Winter, F., & Musliu, N. (2021). A large neighborhood search approach for the paint shop scheduling problem. Journal of Scheduling, 25(4), 453–475. https://doi.org/10.1007/s10951-021-00713-7
Project: ARTIS (2017–2024) -
Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry
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Winter, F., & Musliu, N. (2021). Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry. ACM Transactions on Intelligent Systems and Technology, 12(2), 1–25. https://doi.org/10.1145/3430710
Project: ARTIS (2017–2024) -
Solving the paintshop scheduling problem with memetic algorithms
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Musliu, N., Weintritt, W., & Winter, F. (2021). Solving the paintshop scheduling problem with memetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2021 - Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, EU. https://doi.org/10.1145/3449639.3459375
Project: ARTIS (2017–2024) -
Automated configuration of parallel machine dispatching rules by machine learning
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Faustmann, G., Mrkvicka, C., Musliu, N., & Winter, F. (2021). Automated configuration of parallel machine dispatching rules by machine learning. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO 2021 - Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, EU. https://doi.org/10.1145/3449726.3459541
Project: ARTIS (2017–2024) -
Physician Scheduling During a Pandemic
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Geibinger, T., Kletzander, L., Krainz, M., Mischek, F., Musliu, N., & Winter, F. (2021). Physician Scheduling During a Pandemic. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 456–465). https://doi.org/10.1007/978-3-030-78230-6_29
Project: ARTIS (2017–2024) -
Minimizing Cumulative Batch Processing Time for an Industrial Oven Scheduling Problem
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Lackner, M.-L., Mrkvicka, C., Musliu, N., Walkiewicz, D., & Winter, F. (2021). Minimizing Cumulative Batch Processing Time for an Industrial Oven Scheduling Problem. In 27th International Conference on Principles and Practice of Constraint Programming, {CP} 2021, Montpellier, France (Virtual Conference), October 25-29, 2021} (pp. 37:1-37:18). https://doi.org/10.4230/LIPIcs.CP.2021.37
Project: ARTIS (2017–2024) -
Explaining Propagators for String Edit Distance Constraints
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Musliu, N., Winter, F., & Stuckey, P. J. (2020). Explaining Propagators for String Edit Distance Constraints. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 1676–1683). https://doi.org/10.1609/aaai.v34i02.5530
Project: ARTIS (2017–2024) - Modeling and solving staff scheduling with partial weighted maxSAT / Demirovic, E., Musliu, N., & Winter, F. (2019). Modeling and solving staff scheduling with partial weighted maxSAT. Annals of Operations Research, 275(1), 79–99. https://doi.org/10.1007/s10479-017-2693-y
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Solution Approaches for an Automotive Paint Shop Scheduling Problem
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Winter, F., Musliu, N., Mrkvicka, C., & Demirovic, E. (2019). Solution Approaches for an Automotive Paint Shop Scheduling Problem. In Proceedings of the Twenty-Ninth International Conference on Automated Planning and Scheduling, {ICAPS} 2019 (pp. 573–581). AAAI Press. http://hdl.handle.net/20.500.12708/57874
Project: ARTIS (2017–2024) -
Exact Methods for a Paint Shop Scheduling Problem from the Automotive Supply Industry
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Winter, F., & Musliu, N. (2019). Exact Methods for a Paint Shop Scheduling Problem from the Automotive Supply Industry. CPAIOR 2019 - 16th International Conference on the Integration of Constraint Programming, Artificial Intelligence and Operations Research, Thessaloniki, Greece, EU. http://hdl.handle.net/20.500.12708/86907
Project: ARTIS (2017–2024) -
Modeling and Solving an Automotive Paint Shop Scheduling Problem
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Winter, F., Musliu, N., Demirovic, E., & Mrkvicka, C. (2018). Modeling and Solving an Automotive Paint Shop Scheduling Problem. In 12th International Conference on the Practice and Theory of Automated Timetabling (pp. 477–480). http://hdl.handle.net/20.500.12708/57476
Project: ARTIS (2017–2024) -
Solution-Based Phase Saving and MaxSAT for Employee Scheduling: A Computational Study
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Winter, F., Musliu, N., Demirovic, E., & Stuckey, P. J. (2018). Solution-Based Phase Saving and MaxSAT for Employee Scheduling: A Computational Study. In 12th International Conference on the Practice and Theory of Automated Timetabling (pp. 453–457). http://hdl.handle.net/20.500.12708/57475
Project: ARTIS (2017–2024) -
Paint Shop Scheduling in the Automotive Supply Industry
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Winter, F., Musliu, N., Demirovic, E., & Mrkvicka, C. (2018). Paint Shop Scheduling in the Automotive Supply Industry. In 29th European Conference on Operational Research (p. 161). http://hdl.handle.net/20.500.12708/57470
Project: ARTIS (2017–2024) -
A Hybrid Approach for the Sudoku Problem: Using Constraint Programming in Iterated Local Search
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Musliu, N., & Winter, F. (2017). A Hybrid Approach for the Sudoku Problem: Using Constraint Programming in Iterated Local Search. IEEE Intelligent Systems, 32(2), 52–62. https://doi.org/10.1109/mis.2017.29
Project: ARTE (2012–2017) -
MaxSAT modeling and metaheuristic methods for the employee scheduling problem
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Winter, F. (2016). MaxSAT modeling and metaheuristic methods for the employee scheduling problem [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2016.36862
Download: PDF (812 KB) - Modeling and solving staff scheduling with partial weighted maxSAT / Demirovic, E., Musliu, N., & Winter, F. (2016). Modeling and solving staff scheduling with partial weighted maxSAT. In PATAT 2016: Proceedings of the 11th International Conference of the Practice and Theory of Automated Timetabling (p. 17). http://hdl.handle.net/20.500.12708/56872
Supervisions
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Solving the paintshop scheduling problem with memetic algorithms
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Weintritt, W. (2020). Solving the paintshop scheduling problem with memetic algorithms [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.80201
Download: PDF (1.24 MB)