Practical Discrete Optimization and Decision Modeling

Every day, organizations face complex decisions, from scheduling personnel to routing transport vehicles efficiently. Mathematical optimization provides a systematic way to find the best possible solutions to these high-stakes resource allocation problems. This text-based course guides you from the foundational terminology of discrete optimization to advanced modeling techniques. You will learn how to translate complex real-world scenarios into structured mathematical models that modern computer solvers can execute efficiently. What you'll learn: - Understand the fundamental concepts of linear, integer, and discrete programming. - Formulate clear mathematical models for scheduling, routing, and resource allocation challenges. - Apply modern Python-based modeling libraries to write clean, maintainable optimization code. - Implement advanced modeling techniques such as constraint programming and logical relations. - Configure and tune modern solvers to find optimal solutions quickly and efficiently. - Integrate optimization models into broader data analysis and decision-making pipelines. The course begins with core definitions and basic linear models before progressing to complex discrete constraints and advanced formulation strategies. You will read through practical case studies and analyze code snippets to build your modeling intuition step-by-step. Designed for beginners interested in operations research, data science, or logistics, this course requires no prior optimization experience, though basic programming familiarity is helpful. Start mastering the art of mathematical decision-making today.