Dr. Mohammed Majthoub Almoghrabi

 

Fakultät II - Mathematik und Naturwissenschaften
Institut für Mathematik, Sekr. MA 5-2
Technische Universität Berlin
Straße des 17. Juni 136
10623 Berlin

Office: MA 678
Telephone: +49 30 314 21095
Email: majthoub # at # math.tu-berlin.de

Publications

2025

• Surgery scheduling in flexible operating rooms by using a convex surrogate model of second-stage costs (Mohammed Majthoub Almoghrabi and Guillaume Sagnol)
  Eur. J. Oper. Res., Elsevier, 321(1):23–40, 2025.
  PDF doi arxiv Bibtex
  @article{majthoub2025surgery,
  title = {Surgery scheduling in flexible operating rooms by using a convex surrogate model of second-stage costs},
  author = {Almoghrabi, Mohammed Majthoub and Sagnol, Guillaume},
  journal = {Eur. J. Oper. Res.},
  volume = {321},
  number = {1},
  pages = {23--40},
  year = {2025},
  publisher = {Elsevier},
  arxiv = {2304.13670},
  doi = {10.1016/j.ejor.2024.07.036},
  }
  
• Integer and Unsplittable Multiflows in Series-Parallel Digraphs (Mohammed Majthoub Almoghrabi, Martin Skutella, and Philipp Warode)
  IPCO 2025 – Proc. 26th Conference on Integer Programming and Combinatorial Optimization, Springer, pp. 427–441.
  doi Bibtex
  @inproceedings{MajthoubSkutellaWarode2025,
  author = {Almoghrabi, Mohammed Majthoub and Skutella, Martin and Warode, Philipp},
  editor = {Nicole Megow and
  Amitabh Basu},
  title = {Integer and Unsplittable Multiflows in Series-Parallel Digraphs},
  booktitle = {IPCO 2025 – Proc. 26th Conference on Integer Programming and Combinatorial Optimization},
  pages = {427--441},
  year = {2025},
  doi = {10.1007/978-3-031-93112-3_31},
  publisher = {Springer},
  }
  

2024

• Evaluating the Potential of Reinforcement Learning for Stochastic Machine Scheduling Problems (Mohammed Majthoub Almoghrabi and Guillaume Sagnol)
  2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 593–597.
  doi Bibtex
  @inproceedings{majthoub2024evaluating,
  title = {Evaluating the Potential of Reinforcement Learning for Stochastic Machine Scheduling Problems},
  author = {Almoghrabi, Mohammed Majthoub and Sagnol, Guillaume},
  booktitle = {2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)},
  pages = {593--597},
  year = {2024},
  organization = {IEEE},
  doi = {10.1109/ICTAI62512.2024.00089},
  }
  

Awards

• 3rd Place in the 14th AIMMS-MOPTA Optimization Modeling Competition
  (Mohammed Majthoub Almoghrabi, Guillaume Sagnol and Przemyslaw Bartman)
  14th AIMMS-MOPTA Optimization Modeling Competition
  

Software

• Stochastic Parallel Machine Scheduling
  Python Package
  • Machine learning-based approach for a stochastic scheduling problem on unrelated parallel machines.
  • State-of-the-art heuristic algorithms, serving as benchmarks for stochastic scheduling.
  
  
• Elective and Emergency Surgery Planning
  Python Package
  • Algorithm for solving a real-world operating room scheduling problem, combining elective and emergency surgeries.
  • Heuristic methods to serve as performance benchmarks.
  • Interactive demonstrator accessible at: https://wsgi.math.tu-berlin.de/esp_demonstrator/
  docs source
  
  
• Single-Source Unsplittable Flow
  Python Package
  • Heuristic algorithm to address the single-source unsplittable flow problem with upper and lower bounds.
  docs source