ML-Based Scheduling: A Paradigm Shift
Published:Dec 27, 2025 16:33
•1 min read
•ArXiv
Analysis
This paper surveys the evolving landscape of scheduling problems, highlighting the shift from traditional optimization methods to data-driven, machine-learning-centric approaches. It's significant because it addresses the increasing importance of adapting scheduling to dynamic environments and the potential of ML to improve efficiency and adaptability in various industries. The paper provides a comparative review of different approaches, offering valuable insights for researchers and practitioners.
Key Takeaways
- •The paper provides a comprehensive review of machine-learning-based scheduling methods.
- •It compares solver-centric and data-centric approaches.
- •It discusses challenges and future directions in scalability, reliability, and universality.
- •The focus is on adaptive, intelligent, and trustworthy scheduling systems.
Reference
“The paper highlights the transition from 'solver-centric' to 'data-centric' paradigms in scheduling, emphasizing the shift towards learning from experience and adapting to dynamic environments.”