Introduction to Operations Research for Decision Making

In today's complex business and engineering environments, making decisions based on intuition alone is no longer enough. This course introduces you to the systematic, mathematical approach of Operations Research, enabling you to translate real-world resource constraints into optimal solutions. You will learn to formulate and solve quantitative models that maximize efficiency and minimize costs. By reading through this structured curriculum, you will transform from a beginner into a confident analytical thinker who can model business scenarios mathematically. You will understand how to set up decision variables, define objective functions, and apply algorithmic methods to find the best possible outcomes for logistics, scheduling, and resource allocation problems. What you'll learn: - Understand the core principles, terminology, and history of Operations Research - Formulate linear programming models for production, blending, and transportation problems - Solve optimization models using the Simplex method and graphical analysis - Apply duality theory and sensitivity analysis to evaluate changes in resource availability - Model network flow problems including shortest path and maximum flow challenges - Implement basic integer programming and assignment models for project scheduling - Utilize modern python-based optimization libraries to solve models computationally The course begins with foundational concepts and mathematical definitions, ensuring you grasp the core theory before moving on to step-by-step model formulations and practical optimization scenarios. You will progress from simple two-variable graphical solutions to complex multi-variable models designed for modern business challenges. This text-based course is designed for beginners, students, and professionals in engineering, business analysis, or computer science who want to build a strong foundation in optimization. No prior experience with operations research or advanced mathematical modeling is required.