Operations Research: Linear Programming, Networks, Integer and Stochastic Models by Yong Wang

About this course

Better decisions rarely come from intuition alone—they come from models that turn constraints, costs, time and uncertainty into clear recommendations. This free online course in Operations Research helps you build that decision-making toolkit, moving from the fundamentals of optimization to practical modeling patterns used in analytics teams, logistics, product planning and revenue management.

You’ll learn how linear programming captures real business trade-offs, how feasible regions and extreme points explain why optimal solutions appear where they do, and how concepts like slack and binding constraints translate into real capacity and resource limits. As you progress, you’ll connect the geometry of optimization with algorithmic thinking through the simplex method, building confidence in how solutions are found, how to diagnose special cases, and what it means when multiple optima or other edge conditions appear.

The course also develops the perspective that optimization is not just about finding a best number—it’s about understanding robustness. You’ll explore sensitivity analysis and duality to interpret shadow prices, trade-offs, and the value of resources, strengthening your ability to defend recommendations with evidence. From there, you’ll model structured problems in transportation, transshipment and assignment, then step into network optimization for shortest paths, spanning trees, maximum flow and project networks—core building blocks for modern operations, supply chains and platform systems.

To address decisions that require yes/no or whole-number choices, you’ll learn integer programming ideas and methods such as branch and bound, with classic applications like knapsack, scheduling and the traveling salesman problem. You’ll also extend your skills beyond deterministic settings with decision trees, game-theoretic reasoning, and stochastic processes including Markov chains—useful for customer behavior modeling, system states and long-run performance analysis. Inventory and pricing-focused models such as EOQ, EPQ, newsvendor, revenue management and overbooking help connect optimization to everyday operational levers.

Finally, you’ll see how these models translate into implementable solutions with AMPL and practical solver workflows, enabling you to express decisions, constraints and objectives cleanly and solve them efficiently. If you want career-relevant skills in data science and business intelligence—where optimizing under constraints is as important as predicting—this course offers a rigorous, end-to-end path from formulation to interpretation.