Applied Optimization with Python and Gurobi

“This course contains the use of artificial intelligence.”
This hands-on course teaches applied optimization using Python and the Gurobi solver, with a strong focus on practical modeling and AI-assisted development in the Cursor IDE.

Core topics include:

• Linear Programming – production problems, duality, shadow prices, Simplex method, reduced costs, and slack variables.

• Integer & Binary Programming – BigM method, MIPGap, solution control, and general constraints (absolute value, min/max, indicator).

• Assignment & Network Problems – assignment problem logic, unimodularity, shortest path, and minimum spanning tree.

• NonLinear Programming – quadratic programming (QP), quadratically constrained programming (QCP), piecewise linear functions, and a warehouse location project.

• Multiobjective optimization – Pareto optimality, weighted sum, lexicographic methods, and portfolio case study.

• Advanced techniques – Irreducible Inconsistent Subsystem (IIS), multiscenario analysis, TSP with subtour elimination, lazy constraints, solution pool, warm starts (MIPStart), and tuning MIPFocus.

• Data integration – connecting Gurobi to Excel and Pandas, matrix-based modeling with addMVar, and performance tuning (CPU threads, TimeLimit).

• Machine learning connections – exact clustering and feature selection using MILP.

• Fun challenges – Sudoku, Magic Square, NQueens, Jealous Husbands, and an AI-driven coding challenge.

Target audience: Students of industrial engineering, applied math, computer science, and management; operations research professionals; data scientists; and developers.

Prerequisites: Basic Python and introductory optimization knowledge.

Goals: Master problem formulation, build production-ready pipelines, use AI for rapid development, and solve real-world logistics, scheduling, and ML integration projects.