Python codes for logistics optimization problems

Agentic AI-based route optimizer for logistics and delivery. Simulates 5 warehouses and vehicles using GA, ACO, HC, and OR-Tools. Integrates live traffic & weather data, auto-reoptimization, and a Streamlit-Folium dashboard with optional OSMnx maps.

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Analyzed logistics demand and fleet utilization data to identify pricing and routing inefficiencies. Built a demand-forecasting and route-optimization model to improve fleet utilization and delivery planning. Pilot analysis indicated a 12–16% reduction in fuel consumption, with a corresponding increase in utilization from 78% to 92%.