Introduction to Optimization Methods and Model Building

How do we make the best possible decisions when resources are limited? Optimization methods provide the mathematical framework to solve complex resource allocation, scheduling, and design challenges across various industries. This text-only course guides you from the historical origins of optimization to modern model-building techniques. You will learn how to translate real-world constraints and objectives into precise mathematical models that can be solved computationally. What you'll learn: - Understand the historical evolution of optimization from classical calculus to modern operations research. - Formulate linear and non-linear programming models to represent real-world constraints and objectives. - Apply foundational optimization algorithms to solve resource allocation and decision-making problems. - Explore modern computational modeling workflows and sensitivity analysis. - Analyze how changes in system parameters affect optimal solutions and decision outcomes. You will start with core terminology, historical milestones, and fundamental concepts of mathematical modeling before progressing to practical formulation strategies and modern computational approaches. Designed for beginners in engineering, economics, data science, or business analysis, this course requires no advanced mathematical background. Start reading today to build a strong foundation in optimization theory and practical model construction.