Warehouse Cost Optimization Project

This project demonstrates how data-driven optimization can reduce supply chain costs. It integrates:

PostgreSQL for storing warehouse, product, and shipment data.
Python (Pandas, SQLAlchemy, PuLP, Matplotlib, Seaborn) for data analysis, visualization, and optimization.
Linear Programming to minimize total warehouse operating + inventory costs while meeting demand and capacity constraints.
Real-world application: Companies can save millions in logistics costs by running such optimizations.