2024W

• Bachelor Thesis / 184.691 / PR
• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR

2025S

• Bachelor Thesis / 184.691 / PR
• Database Systems / 184.686 / VU
• Problem Solving and Search in Artificial Intelligence / 181.190 / VU

• CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling
  2017 – 2025 / Christian Doppler Research Association (CDG)
  Publications: 136808 / 139849 / 138379 / 138399 / 138400 / 138528 / 138530 / 144345 / 142199 / 142211 / 142210 / 142193 / 141227 / 141228 / 143220 / 143222 / 190688 / 191173 / 193566 / 192686 / 192678 / 203673 / 204355 / 209771 / 209950 / 209926 / 209911 / 209932 / 208556 / 209917 / 210249 / 208780 / 210368 / 210992 / 213563 / 213929 / 213924 / 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

2025

• Multi-neighborhood simulated annealing for the oven scheduling problem / Da Ros, F., Di Gaspero, L., Lackner, M.-L., Musliu, N., & Winter, F. (2025). Multi-neighborhood simulated annealing for the oven scheduling problem. COMPUTERS & OPERATIONS RESEARCH, 177, Article 106999. https://doi.org/10.1016/j.cor.2025.106999
  Project: ARTIS (2017–2025)

2024

• A Hybrid Approach for the Artificial Teeth Scheduling Problem / Winter, F., & Musliu, N. (2024). A Hybrid Approach for the Artificial Teeth Scheduling Problem. In Proceedings of the 14th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2024 (pp. 255–258).
  Project: ARTIS (2017–2025)
• Solving the Employee Task Distribution Problem with Multiple Objectives / Horn, M., Lackner, M.-L., Mrkvicka, C., Musliu, N., Preininger, J., & Winter, F. (2024). Solving the Employee Task Distribution Problem with Multiple Objectives. In Proceedings of the 14th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2024 (pp. 36–51).
  Project: ARTIS (2017–2025)
• Local Search Algorithms for the Oven Scheduling Problem / Da Ros, F., Di Gaspero, L., Lackner, M.-L., Musliu, N., & Winter, F. (2024). Local Search Algorithms for the Oven Scheduling Problem. In GECCO ’24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 191–194). https://doi.org/10.1145/3638530.3654158
  Project: ARTIS (2017–2025)

2023

• Exact methods for the Oven Scheduling Problem / Lackner, M.-L., Mrkvicka, C., Musliu, N., Walkiewicz, D., & Winter, F. (2023). Exact methods for the Oven Scheduling Problem. Constraints, 28(2), 320–361. https://doi.org/10.1007/s10601-023-09347-2
  Download: PDF (731 KB)
  Project: ARTIS (2017–2025)

2022

• Modeling and Solving Parallel Machine Scheduling with Contamination Constraints in the Agricultural Industry / Winter, F., Meiswinkel, S., Musliu, N., & Walkiewicz, D. (2022). Modeling and Solving Parallel Machine Scheduling with Contamination Constraints in the Agricultural Industry. In 28th International Conference on Principles and Practice of Constraint Programming, CP 2022, July 31 to August 8, 2022, Haifa, Israel (pp. 1–18). https://doi.org/10.4230/LIPIcs.CP.2022.41
  Download: PDF (859 KB)
  Project: ARTIS (2017–2025)
• Exact and meta-heuristic approaches for the production leveling problem / 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 (2017–2025)
• An Investigation of Hyper-Heuristic Approaches for Teeth Scheduling / 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 (2017–2025)
• Modeling and Solving the K-track Assignment Problem / 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 (2017–2025)
• A Hybrid Approach for Paint Shop Scheduling in the Automotive Supply Industry / 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 (2017–2025)
• Solving the Production Leveling Problem with Order-Splitting and Resource Constraints / 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 (2017–2025)
• Solving an Industrial Oven Scheduling Problem with a Simulated Annealing Approach / 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 (2017–2025)

2021

• Minimizing Cumulative Batch Processing Time for an Industrial Oven Scheduling Problem / 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 (2017–2025)
• Automated configuration of parallel machine dispatching rules by machine learning / 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. https://doi.org/10.1145/3449726.3459541
  Project: ARTIS (2017–2025)
• Solving the paintshop scheduling problem with memetic algorithms / 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. https://doi.org/10.1145/3449639.3459375
  Project: ARTIS (2017–2025)
• Automated Production Scheduling for Artificial Teeth Manufacturing / 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 (2017–2025)
• Automated scheduling for automotive supplier paint shops and teeth manufacturing / 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)
• A large neighborhood search approach for the paint shop scheduling problem / 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 (2017–2025)
• Exact and metaheuristic approaches for unrelated parallel machine scheduling / 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 (2017–2025)
• Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry / 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 (2017–2025)
• Physician Scheduling During a Pandemic / 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 (2017–2025)

2020

• Explaining Propagators for String Edit Distance Constraints / 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 (2017–2025)

2019

• Solution Approaches for an Automotive Paint Shop Scheduling Problem / 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 (2017–2025)
• Exact Methods for a Paint Shop Scheduling Problem from the Automotive Supply Industry / 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. http://hdl.handle.net/20.500.12708/86907
  Project: ARTIS (2017–2025)
• 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

2018

• Paint Shop Scheduling in the Automotive Supply Industry / 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 (2017–2025)
• Modeling and Solving an Automotive Paint Shop Scheduling Problem / 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 (2017–2025)
• Solution-Based Phase Saving and MaxSAT for Employee Scheduling: A Computational Study / 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 (2017–2025)

2017

• A Hybrid Approach for the Sudoku Problem: Using Constraint Programming in Iterated Local Search / 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)

2016

• MaxSAT modeling and metaheuristic methods for the employee scheduling problem / 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

• Optimizing the cell assignment problem / Eischer, A. M. (2024). Optimizing the cell assignment problem [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.126921
  Download: PDF (889 KB)
• Solution approaches for balanced task planning and employee task distribution / Nagler, F. (2024). Solution approaches for balanced task planning and employee task distribution [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.117505
  Download: PDF (1020 KB)
• Solving the paintshop scheduling problem with memetic algorithms / 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)