Stochastic Models in Operations Research

Where IE 256 gave you the deterministic toolkit, this course is about decision-making when the system itself is random, modeling processes whose future state depends on probability rather than a fixed recurrence, and extracting performance measures (long-run costs, throughput, optimal policies) from that randomness. You'll spend the semester building and solving Markov chains, MDPs, renewal and queueing models through four problem-set-heavy homeworks plus a midterm and final, with stochastic dynamic programming on inventory problems as the main bridge between theory and application. It's the prerequisite mindset for IE 400-level electives in supply chain, revenue management, and simulation, and the reason "expected value" stops being a back-of-envelope estimate and starts being a design tool.