[Zurück]
Alexander Kinast, BSc MSc
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Kinast, A., Braune, R., Dörner, K. F., & Rinderle-Ma, S. (2023). Optimizing the Solution Quality of Metaheuristics Through Process Mining Based on Selected Problems from Operations Research. in C. Di Francescomarino, A. Burattin, C. Janiesch, & S. Sadiq (Hrsg.), Business Process Management Forum - BPM 2023 Forum, Proceedings (S. 232-248). Springer. Lecture Notes in Business Information Processing Band 490 https://doi.org/10.1007/978-3-031-41623-1_14
Kinast, A., Braune, R., Dörner, K. F., Rindlerle-Ma, S., & Weckenborg, C. (2022). A hybrid metaheuristic solution approach for the cobot assignment and job shop scheduling problem. Journal of Industrial Information Integration, 28, [100350]. https://doi.org/10.1016/j.jii.2022.100350
Kinast, A., Dörner, K. F., & Rinderle-Ma, S. (2022). Combing metaheuristics and process mining: Improving cobot placement in a combined cobot assignment and job shop scheduling problem. in Procedia Computer Science (Band 200, S. 1836-1845). Elsevier. https://doi.org/10.1016/j.procs.2022.01.384
Kinast, A., Dörner, K., & Rinderle-Ma, S. (2021). Biased random-key genetic algorithm for cobot assignment in an assembly/disassembly job shop scheduling problem. Beitrag in International Conference on Industry 4.0 and Smart Manufacturing 2020, Unbekannt/undefiniert. http://eprints.cs.univie.ac.at/6568/
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Institut für Business Decisions and Analytics

Oskar-Morgenstern-Platz 1
1090 Wien
Zimmer: 06.621

alexander.kinast@univie.ac.at