AI Logistics Scheduling Optimization: 28% Cost Reduction in Regional Distribution Network, On-Time Rate Increased to 97%
How a regional express delivery company serving East China (50,000 orders per day) introduced AI route optimization and dynamic scheduling systems to reduce distribution costs by 28%, increase on-time rate from 82% to 97%, and cut carbon emissions by 15%. The case shares the complete process from selection, deployment to continuous optimization.
Steps
- 1
Data infrastructure construction: Integrate historical delivery data (routes, timeliness, costs), real-time traffic API (Amap), and station capacity data to establish a unified data platform.
- 2
AI route planning: Use Google OR-Tools to build a Vehicle Routing Problem (VRP) model, automatically generating the optimal delivery routes for the entire day at 6 AM daily, considering time windows, load limits, and driver working hours.
- 3
Dynamic scheduling: Monitor delivery progress in real-time; when delays occur (accidents/traffic changes), AI re-plans delivery routes for affected areas within 30 seconds.
- 4
Loading optimization: Based on order package dimensions, weight, and destination concentration, AI optimizes vehicle loading sequence to reduce handling times and increase loading rate by 15%.
- 5
Effect monitoring dashboard: Build a Tableau dashboard to display real-time cost, timeliness, and driver scores for each route, and generate monthly optimization suggestion reports.
Recommended tools
Also available in 中文.