Multi-Objective Decision Analysis

Real decisions rarely have a single yardstick, you trade cost against risk, speed against quality, and somebody's preferences have to be made explicit before any optimizer can help. This course gives you the machinery to do that honestly: building value hierarchies, eliciting utility functions, handling risk aversion across multiple attributes, and scalarizing biobjective problems so they become solvable. Expect to structure messy problems, fit value and utility functions, and work through spreadsheet-based cases alongside the optimization theory. It sits downstream of your OR and probability sequence and is what makes the modeling you've learned actually usable when stakeholders disagree.