Course Websites

The course will cover topics related to optimization over large-scale networks. We will look at data-driven methodologies by which very large-scale optimization problems, primarily integer programs, can be solved. We will consider motivations from application areas such as airline scheduling, vehicle routing, and communications. Topics covered include shortest paths; multi-commodity flows; decomposition techniques; Lagrangean relaxation; set-covering and set-partitioning problems (with special characteristics); column generation and branch-and-price and cut; composite variables; large-scale neighborhood search techniques; modeling robustness and uncertainty; stochastic modeling in large-scale integer programs; data-driven optimization. The course will include real-world modeling examples from applications including vehicle routing, freight logistics, and airline schedule planning. Course Information: 4 graduate hours. No professional credit. Prerequisite: IE 411 or the equivalent.