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AI Research
arXiv CS.AI·May 25·8 min read
CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem
TL;DR
Scheduling problems with complex job dependencies are traditionally solved using either Dynamic Programming (DP) or Constraint Programming (CP) separately. Researchers combined both paradigms by using DP as the main search strategy while leveraging CP's constraint propagation as a helper subroutine for the Partial Shop Scheduling Problem.
✦ Why It Matters
Engineers can leverage hybrid DP-CP approaches to solve complex scheduling problems with flexible constraint modeling and anytime solution improvement.
Key Takeaways
- Explore hybrid approaches combining DP and CP for complex scheduling.
- Utilize anytime strategies to improve solution quality.
- Incorporate arbitrary precedence constraints in scheduling models.
- Implement Large Neighborhood Search to enhance incumbent solutions.
How It Works
The hybrid approach uses Dynamic Programming as the primary search method, while Constraint Programming acts as a subroutine to manage global constraints. This allows for more flexible scheduling, accommodating various precedence constraints and enabling anytime strategies that adaptively refine solutions.