Operations Research and Optimization Fundamentals

To excel in quantitative examinations and modern analytical roles, you need a rock-solid grasp of optimization models and mathematical decision theory. This course clarifies the complex mathematics behind resource allocation, network analysis, and strategic decision-making. You will learn to formulate real-world constraints into solvable mathematical models, preparing you thoroughly for advanced exams and analytical challenges. Starting with foundational definitions, core terminology, and historical context, you will systematically build your understanding of mathematical optimization. What you'll learn: Understand the mathematical foundations of linear programming problems and graphical solution methods; Apply the Simplex algorithm, duality theory, and sensitivity analysis to resource allocation models; Solve transportation and assignment problems using systematic algorithmic approaches; Analyze network models, including critical path methods and project evaluation techniques; Formulate game theory models to analyze strategic decision-making scenarios. This text-based course guides you through theoretical explanations, step-by-step mathematical proofs, and comprehensive practice problems. This course is designed for university students, math enthusiasts, and aspirants preparing for competitive quantitative examinations, requiring no prior exposure to advanced optimization theory. Start reading today to master the mathematical frameworks of operations research.