Linear Programming in Python with PuLP

Every business and technical organization faces the challenge of allocating limited resources to maximize efficiency, minimize costs, or increase profits. This text-based course introduces you to the power of linear programming and optimization, showing you how to translate complex decision-making problems into clean, executable Python code. You will transition from manual, error-prone planning to automated, mathematically optimal solutions. By reading through structured explanations and analyzing clear code examples, you will learn how to formulate objective functions, establish realistic constraints, and programmatically solve optimization models. The course starts with foundational concepts, defining key terminology like decision variables, feasibility, and bounds, before guiding you through hands-on modeling scenarios. What you'll learn: - Understand the core principles of linear programming and mathematical optimization. - Define decision variables, objective functions, and constraints within Python. - Model real-world business scenarios such as resource allocation and production planning using the PuLP library. - Configure different optimization solvers and interpret their status outputs and sensitivity results. - Apply clean coding practices, including Python type hints and structured data models, to keep your optimization scripts maintainable. - Troubleshoot infeasible or unbounded models to ensure your scripts deliver actionable decisions. The course begins with an introduction to optimization concepts and basic Python setups, then progresses to writing your first PuLP models, and concludes with structuring complex multi-variable problems for real-world application. This course is designed specifically for beginners to optimization, requiring only a basic familiarity with Python syntax and no advanced mathematical background. Start reading today to unlock the power of mathematical decision-making in your code.