Python Optimization: Solve Operations Research Problems

Modern organizations face complex decision-making challenges daily, from logistics routing to resource allocation. Solving these problems efficiently requires mathematical optimization, one of the most practical and valued skills in data science and operations research. This text-based course guides you from the absolute basics of Python optimization to modeling and solving real-world decision problems. You will learn how to translate business constraints into mathematical models and solve them programmatically using industry-standard libraries and solvers. What you'll learn: - Understand the foundational concepts of linear, integer, and non-linear programming. - Model business constraints and objective functions using Python libraries like Pyomo, PuLP, and OR-Tools. - Configure and apply open-source solvers such as GLPK and IPOPT to find optimal solutions. - Implement metaheuristic algorithms, including genetic algorithms and particle swarm optimization, for non-convex problems. - Structure optimization workflows cleanly using modern Python practices, virtual environments, and structured data handling. - Solve practical case studies in resource allocation, route optimization, and capacity planning. You will start with core mathematical concepts and basic Python setup before moving step-by-step through linear programming, integer programming, and advanced metaheuristics. Each concept is reinforced with clear written explanations, code snippets, and structured modeling exercises. This course is designed for beginners, data analysts, and engineers who want to learn operations research from scratch, with no prior optimization experience required. Begin your journey into mathematical modeling and start making optimal, data-driven decisions today.