Foundations of Operations Research and Optimization in R

Making critical organizational decisions should not rely on guesswork. Operations research provides a scientific, data-driven framework to model complex scenarios and find the absolute best possible solutions. In this text-based course, you will transition from understanding basic decision-making theory to building and solving your own mathematical models. You will learn how to translate real-world constraints—such as limited budgets, time, and personnel—into mathematical equations and solve them programmatically using R. What you'll learn: - Understand the core terminology, history, and scientific methodology of operations research. - Formulate linear programming models to optimize resources, reduce costs, and maximize operational efficiency. - Translate administrative and business constraints into objective functions and mathematical variables. - Write clean R code using modern optimization packages to programmatically solve complex models. - Analyze sensitivity and interpret solver outputs to make actionable, data-backed decisions. This course begins with foundational concepts, defining the core components of mathematical models, variables, and constraints before introducing any code. You will then move step-by-step through practical scenarios, learning how to structure, write, and execute optimization models in R. Designed specifically for beginners, this course requires no prior experience in operations research or advanced mathematics. Step into the world of quantitative analysis and start optimizing your decisions today.