Operations Research: Foundations of Optimization and Decision-Making

In today's data-driven world, making the most efficient use of limited resources is critical for any organization. This text-based course introduces you to the core principles of Operations Research, guiding you from basic mathematical modeling to solving complex decision-making problems. You will develop a strong analytical foundation to formulate, analyze, and solve optimization problems. By reading through structured explanations and working through step-by-step written exercises, you will learn how to translate real-world constraints into mathematical formulations and apply modern computational tools to find optimal solutions. What you'll learn: Understand foundational concepts of linear programming, duality, and sensitivity analysis; Formulate mathematical models for transportation, assignment, and network flow problems; Apply deterministic and probabilistic decision-making models to real-world scenarios; Explore modern computational tools and Python-based solvers like PuLP for optimization; Analyze queuing systems and inventory control models to improve operational efficiency; Practice solving optimization problems through detailed, step-by-step written walkthroughs. The course begins with key terminology and the mathematical foundations of linear programming before moving into advanced network models, queuing theory, and modern software-driven optimization techniques. You will progress naturally from theoretical formulation to practical application through clear written explanations. This course is designed for beginners, engineering students, business analysts, and anyone interested in decision science, with no prior advanced mathematical background required. Start building your analytical decision-making skills today through this clear, written guide to operations research.