Marc Huber On Leave

Projektass. / MSc

Role

PreDoc Researcher
Algorithms and Complexity, E192-01

Contact

Publications

A Learning Large Neighborhood Search for the Staff Rerostering Problem / Oberweger, F. F., Raidl, G., Rönnberg, E., & Huber, M. (2022). A Learning Large Neighborhood Search for the Staff Rerostering Problem. In P. Schaus (Ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 300–317). Springer International Publishing. https://doi.org/10.1007/978-3-031-08011-1_20
Learning Beam Search: Utilizing Machine Learning to Guide Beam Search for Solving Combinatorial Optimization Problems / Huber, M., & Raidl, G. (2022). Learning Beam Search: Utilizing Machine Learning to Guide Beam Search for Solving Combinatorial Optimization Problems. In Machine Learning, Optimization, and Data Science (pp. 283–298). Springer Nature Switzerland AG. https://doi.org/10.34726/3443
Download: PDF (322 KB)
A Beam Search for the Shortest Common Supersequence Problem Guided by an Approximate Expected Length Calculation / Mayerhofer, J., Kirchweger, M., Huber, M., & Raidl, G. (2022). A Beam Search for the Shortest Common Supersequence Problem Guided by an Approximate Expected Length Calculation. In Evolutionary Computation in Combinatorial Optimization (pp. 127–142). Springer Nature Switzerland AG. https://doi.org/10.34726/3442
Download: PDF (434 KB)
A Relative Value Function Based Learning Beam Search for Longest Common Subsequence Problems / Huber, M., & Raidl, G. (2022). A Relative Value Function Based Learning Beam Search for Longest Common Subsequence Problems. In A. Quesada-Arencibia, J. C. Rodriguez, R. Moreno-Díaz, G. S. de Blasio, & C. R. Garcia (Eds.), Computer Aided Systems Theory - Extended Abstracts (pp. 22–23). IUCTC Universidad de Las Palmas de Gran Canaria.

Supervisions

Using graph neural networks in local search for edge-based relaxations of the maximum clique problem / Ettrich, R. (2022). Using graph neural networks in local search for edge-based relaxations of the maximum clique problem [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.107601
Download: PDF (1.73 MB)
Minimizing makespan in flow shops with a reinforcement learning like approach : A learning beam search for the no-wait flow shop scheduling problem with release times / Mayerhofer, J. (2022). Minimizing makespan in flow shops with a reinforcement learning like approach : A learning beam search for the no-wait flow shop scheduling problem with release times [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.99461
Download: PDF (4.84 MB)
Computational Methods for fleet scheduling in E-mobility / Varga, J. (2021). Computational Methods for fleet scheduling in E-mobility [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.87627
Download: PDF (1.26 MB)
Learning large neighborhood search for the staff resortering problem / Oberweger, F. F. (2021). Learning large neighborhood search for the staff resortering problem [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.92421
Download: PDF (1.68 MB)