Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Jul 29·5 min read
Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search
TL;DR
An Adaptive Large Neighborhood Search (ALNS) heuristic was developed in Python to effectively tackle large pickup-and-delivery problems, incorporating vehicle routing, time windows, and capacity constraints. This approach significantly optimizes delivery schedules while adhering to mandatory driver breaks.
✦ Why It Matters
Implement the ALNS heuristic in your logistics software to optimize delivery routes and reduce costs.
Key Takeaways
- Use ALNS for complex routing problems with multiple constraints.
- Implement safety rules in logistics planning to enhance driver safety.
- Shift from exact optimization to heuristic methods for scalability.
- Explore the alns Python library for efficient route optimization.
How It Works
ALNS begins with a feasible solution, then iteratively removes parts of it using various destroy operators, followed by a repair phase that reinserts requests while respecting constraints. This method allows for larger adjustments compared to traditional local search techniques, enabling the algorithm to escape local optima and find better solutions.