Foundations of Operations Research: Practical Optimization and Decision Making

How do organizations maximize efficiency, minimize operational costs, and allocate limited resources effectively? Operations research provides the mathematical framework to make these critical decisions with absolute confidence. This text-based course guides you through the core concepts of mathematical modeling, translating complex theories into clear, actionable business strategies. You will transition from understanding foundational definitions to structuring and solving real-world optimization challenges. Through detailed written explanations and step-by-step analytical walkthroughs, you will gain the skills to model scenarios mathematically and find optimal solutions. What you'll learn: - Understand the core principles of linear programming and the simplex method. - Solve transportation, assignment, and network flow problems systematically. - Apply queuing theory and game theory to optimize service systems and strategic interactions. - Model and solve optimization problems using modern Python tools like PuLP. - Analyze project schedules and timelines using CPM and PERT techniques. - Implement decision-making frameworks under conditions of uncertainty and risk. The course begins with key terminology and foundational concepts before moving into practical modeling scenarios. You will progress through structured text-based lessons, exploring real-world case studies and clear code snippets that illustrate how to apply these mathematical techniques in modern industries. This course is designed for absolute beginners, requiring no advanced mathematical background or prior programming experience. It is ideal for aspiring data analysts, business managers, industrial engineers, and problem solvers. Start reading today to master the analytical tools needed to drive optimal business decisions.