Introduction to Decision Modeling and Optimization

Making critical business decisions shouldn't rely on guesswork or intuition alone. This course introduces you to the structured world of decision modeling, where mathematical and logical frameworks help you find the most efficient solutions to complex operational problems. You will transition from subjective decision-making to building robust, data-backed models. By learning how to formulate and solve optimization problems, analyze risks, and evaluate trade-offs, you will gain the skills to optimize resources and improve strategic outcomes in any organization. What you'll learn: Understand foundational decision-making concepts, criteria under uncertainty, and risk profiles; Formulate linear programming models to optimize resource allocation, production, and logistics; Build decision trees and network models to evaluate multi-stage choices and sequential outcomes; Apply Multi-Criteria Decision Analysis (MCDA) to balance competing organizational priorities; Practice implementing basic optimization models using modern tools and Python-based solvers. The course starts with fundamental decision theories and terminology before guiding you step-by-step through mathematical formulation, sensitivity analysis, and practical modern applications. You will work through written scenarios and structured exercises to reinforce your problem-solving skills. This course is designed for beginners, aspiring business analysts, operations managers, and students with no prior background in advanced operations research. Start building structured models to solve your toughest business challenges today.