Logistics

Route Optimization Engine

Executive Summary

Predictive modeling for last-mile delivery that reduced fuel costs by 12%. We combined historical traffic data with real-time constraints to optimize fleet dispatch.

1The Challenge

A regional logistics company was losing margin due to inefficient routing. Their legacy software couldn't account for real-time variables like weather or sudden traffic jams, resulting in wasted fuel and missed delivery windows.

2The Solution

We didn't just use an LLM; we used an LLM as an orchestrator for a traditional optimization algorithm. The AI parses unstructured driver notes and traffic alerts to update the constraints of a mathematical solver (OR-Tools). This hybrid approach combines the reasoning of AI with the mathematical precision of operations research.

3The Result

Fuel costs decreased by 12% across the fleet. On-time delivery rates improved by 18%. The system adapts dynamically to road conditions, saving an estimated $200k annually.

The Tech Stack

Python
Google OR-Tools
Pandas
FastAPI
Redis

Key Takeaway

The best AI solutions often involve 'Hybrid Intelligence'—using LLMs to parse unstructured data and traditional algorithms to solve the math.