This project focuses on optimizing inventory operations using EOQ, JIT, and Newsvendor models. Real inventory data from Namal University is analyzed and optimized using Python-based tools and solvers. Key metrics like holding cost, ordering cost, and lead time are minimized. The solution provides data-driven insights to support efficient and cost-effective inventory decisions.
Keywords: Python Optimization, Inventory Management, Operations Research, Cost Minimization, Supply Chain Management
Tools: Python, Pyomo, SciPy, CVXPY, NumPy, Pandas, Power BI
Department: Department of Mathematics
Tools: Python, Pyomo, SciPy, CVXPY, NumPy, Pandas, Power BI
Department: Department of Mathematics
Project Poster
