Workforce Scheduling & Routing
ML classifier for algorithm selection in scheduling problems — 40% performance improvement
A gradient-boosting classifier that predicts the best optimization algorithm given a problem instance and runtime constraint, achieving 40% higher performance than any single algorithm applied uniformly. This work is the subject of a co-authored paper currently in review.
Key contributions:
- Formulated algorithm selection as a classification problem over scheduling problem features
- Trained gradient-boosting model (scikit-learn) on a diverse benchmark suite
- Achieved 40% performance improvement over best single-algorithm baselines
- Co-author paper submitted for peer review
Stack: Python, Scikit-learn, Pandas, NumPy
Publication: Manalp, C. K., Erol, A. K., Erol, K., & Evrendilek, C. (2024). A Holistic Approach to Workforce Scheduling and Routing.