2024S

• Problem Solving and Search in Artificial Intelligence / 181.190 / VU

• 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

• A System for Automated Industrial Test Laboratory Scheduling / Danzinger, P., Geibinger, T., Janneau, D., Mischek, F., Musliu, N., & Poschalko, C. (2022). A System for Automated Industrial Test Laboratory Scheduling. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/3546871
• Investigating Hyper-heuristics for Real-World Test Laboratory Scheduling / Mischek, F., & Musliu, N. (2022, July 0). Investigating Hyper-heuristics for Real-World Test Laboratory Scheduling [Conference Presentation]. EURO 2022, Espoo, Finland.
• Reinforcement Learning for Cross-Domain Hyper-Heuristics / Mischek, F., & Musliu, N. (2022). Reinforcement Learning for Cross-Domain Hyper-Heuristics. In L. De Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) (pp. 4793–4799). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2022/664
• Automated project scheduling in real-world test laboratories / Mischek, F. (2022). Automated project scheduling in real-world test laboratories [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.107689
  Download: PDF (1.87 MB)
• A local search framework for industrial test laboratory scheduling / Mischek, F., & Musliu, N. (2021). A local search framework for industrial test laboratory scheduling. Annals of Operations Research, 302, 533–562. https://doi.org/10.1007/s10479-021-04007-1
  Download: PDF (477 KB)
• Local search approaches for the test laboratory scheduling problem with variable task grouping / Mischek, F., Musliu, N., & Schaerf, A. (2021). Local search approaches for the test laboratory scheduling problem with variable task grouping. Journal of Scheduling. https://doi.org/10.1007/s10951-021-00699-2
• A local search framework for industrial test laboratory scheduling / Mischek, F., & Musliu, N. (2021). A local search framework for industrial test laboratory scheduling. Annals of Operations Research, 302(2), 533–562. https://doi.org/10.1007/s10479-021-04007-1
  Project: ARTIS (2017–2024)
• Investigating Constraint Programming for Real World Industrial Test Laboratory Scheduling / Geibinger, T., Mischek, F., & Musliu, N. (2021). Investigating Constraint Programming for Real World Industrial Test Laboratory Scheduling. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 304–319). https://doi.org/10.1007/978-3-030-19212-9_20
  Project: ARTIS (2017–2024)
• 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 (2017–2024)
• Project Scheduling in Industrial Test Laboratories / Mischek, F. (2020). Project Scheduling in Industrial Test Laboratories. ICAPS 2020 - International Conference on Automated Planning and Scheduling, Nancy, France, EU. http://hdl.handle.net/20.500.12708/87083
  Project: ARTIS (2017–2024)
• Solving the Test Laboratory Scheduling Problem with Variable Task Grouping / Danzinger, P., Geibinger, T., Mischek, F., & Musliu, N. (2020). Solving the Test Laboratory Scheduling Problem with Variable Task Grouping. In Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling (pp. 357–365). http://hdl.handle.net/20.500.12708/58340
  Project: ARTIS (2017–2024)
• Integer programming model extensions for a multi-stage nurse rostering problem / Mischek, F., & Musliu, N. (2019). Integer programming model extensions for a multi-stage nurse rostering problem. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2623-z
  Project: ARTIS (2017–2024)
• Investigating Constraint Programming for Real World Industrial Test Laboratory Scheduling / Geibinger, T., Mischek, F., & Musliu, N. (2019). Investigating Constraint Programming for Real World Industrial Test Laboratory Scheduling. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 304–319). Springer. https://doi.org/10.1007/978-3-030-19212-9_20
  Project: ARTIS (2017–2024)
• A Local Search Framework for Industrial Test Laboratory Scheduling / Mischek, F., & Musliu, N. (2018). A Local Search Framework for Industrial Test Laboratory Scheduling. In Proceedings of the 12th International Conference on the Practice and Theory of Auto­mated Timetabling (PATAT-2018) (pp. 465–467). http://hdl.handle.net/20.500.12708/57572
  Project: ARTIS (2017–2024)
• Exact and heuristic approaches for a multi-stage nurse rostering problem / Mischek, F. (2016). Exact and heuristic approaches for a multi-stage nurse rostering problem [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2016.36864
  Download: PDF (803 KB)
• Integer Programming and Heuristic Approaches for a Multi-Stage Nurse Rostering Problem / Mischek, F., & Musliu, N. (2016). Integer Programming and Heuristic Approaches for a Multi-Stage Nurse Rostering Problem. In PATAT 2016: Proceedings of the 11th International Conference of the Practice and Theory of Automated Timetabling (p. 18). http://hdl.handle.net/20.500.12708/56871