Till Porrmann
PEVZ-ID
2 Publikationen
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2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984133Römer, M., Hagemann, F., & Porrmann, T. (2023). Predict, Tune and Optimize for Data-Driven Shift Scheduling with Uncertain Demands. In M. Sellmann & K. Tierney (Eds.), Lecture Notes in Computer Science. Learning and Intelligent Optimization. 17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers (pp. 254-269). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-44505-7_18PUB | DOI
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2021 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2958571Porrmann, T., & Römer, M. (2021). Learning to Reduce State-Expanded Networks for Multi-activity Shift Scheduling. In P. J. Stuckey (Ed.), Lecture Notes in Computer Science: Vol. 12735. Integration of Constraint Programming, Artificial Intelligence, and Operations Research. 18th International Conference, CPAIOR 2021, Vienna, Austria, July 5–8, 2021, Proceedings (pp. 383-391). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-78230-6_24PUB | DOI