Operations Research: Linear and Integer Programming

In today's data-driven world, making the most efficient use of limited resources is critical for business success. This course introduces you to operations research, showing you how to translate complex decision-making challenges into solvable mathematical models. Through clear, text-based explanations and step-by-step breakdowns, you will move from understanding basic optimization concepts to formulating and solving complex linear and integer programming problems. You will learn how to model real-world constraints in logistics, finance, and production, and discover how modern computational tools find optimal solutions. What you'll learn: Understand the foundational concepts of operations research and optimization modeling; Formulate linear programming models to maximize efficiency and minimize costs; Apply integer and mixed-integer programming to represent discrete, yes/no decisions; Translate verbal business constraints into precise mathematical inequalities; Explore modern solver concepts and how to structure models for computational tools; Analyze sensitivity and interpret solver outputs to make informed strategic decisions. The course begins with essential definitions and the core principles of mathematical modeling. You will then progress through graphical solutions, algebraic formulations, and advanced integer programming techniques, finishing with practical application scenarios. This course is designed for beginners, students, and professionals in business, engineering, or data analysis who want to learn optimization from the ground up, with no advanced mathematical prerequisites required. Start reading today to master the mathematical frameworks that drive optimal decision-making.