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: Meinolf Sellmann; Kevin Tierney (Hrsg.): Learning and Intelligent Optimization. 17th International Conference, LION 17, Nice, France, June 4–8, 2023, Revised Selected Papers. Cham: Springer International Publishing. (Lecture Notes in Computer Science, ). S. 254-269.PUB | 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: Peter J. Stuckey (Hrsg.): Integration of Constraint Programming, Artificial Intelligence, and Operations Research. 18th International Conference, CPAIOR 2021, Vienna, Austria, July 5–8, 2021, Proceedings. Cham: Springer International Publishing. (Lecture Notes in Computer Science, 12735). S. 383-391.PUB | DOI