optimization-ml-hybrid

What problem does it solve?

This Skill bridges the gap between predictive machine learning models and prescriptive mathematical optimization, enabling more intelligent and data-driven decision-making in supply chain management.

Core Features & Use Cases

• Predict-then-Optimize: Use ML forecasts (e.g., demand) as inputs for optimization models (e.g., production planning).
• ML for Parameters: Train ML models to learn optimal parameters for optimization problems (e.g., safety stock levels).
• End-to-End Learning: Develop systems where optimization is a differentiable layer within a larger neural network.
• Use Case: Forecast product demand using an ML model and then use these forecasts to optimize production schedules and inventory levels.

Quick Start

Use the optimization-ml-hybrid skill to combine machine learning predictions with optimization models.