Free online courseOperations Research: Linear Programming, Networks, Integer and Stochastic Models

Course Description

Learn how to model and solve real decision-making problems with this free online course in Operations Research, focused on linear programming, network optimization, integer models, and stochastic systems. Designed for learners in Technology and Programming with an interest in Data Science and Business Intelligence, it builds a practical foundation for turning complex business and engineering questions into structured optimization models.

You will progress from the essentials of operations research to linear programming concepts such as feasible regions, convexity, extreme points, and graphical solution techniques, then move into the simplex method with key ideas like slack variables, basic feasible solutions, special cases, and goal programming. The course also covers sensitivity analysis and duality, helping you understand how solutions change when inputs and constraints shift.

Beyond core linear programming, you will study transportation, transshipment, and assignment models with common solution strategies, then explore network problems including shortest path, minimum spanning tree, maximum flow, and project scheduling concepts such as critical path and float. Integer programming is introduced through relaxation and branch and bound, alongside classic problems like knapsack, job shop scheduling, and traveling salesman approaches.

To broaden your toolkit, the course touches nonlinear convex programming fundamentals, decision trees, and game theory concepts including Nash equilibrium. Stochastic modeling is addressed through Markov chains, steady-state behavior, mean first passage time, and absorbing chains. Inventory and revenue topics include EOQ, EPQ, newsvendor modeling, fare control, and revenue management. You also get an introduction to implementing models using AMPL and solving them via the NEOS server, connecting theory to hands-on optimization practice.