Advanced Discrete Optimization

This course is a overview of discrete optimization theory and its applications to business settings with known data, which finds its uses when linear and nonlinear programming (LP, NLP) formulations are not possible and integer programming (IP) formulations cannot be solved efficiently due to computational limitations. It covers combinatorial optimization fundamentals, network optimization including shortest path, maximum flows, and minimum spanning trees, as well as graph theory and matching. The computational approaches to be covered include deterministic dynamic programming and discrete algorithm development (both exact and approximate) as well as an introduction to complexity theory concepts such as NP-completeness. Application areas range from operations management (various manufacturing and service settings including retail, healthcare, supply chains), computer science (networks), and economics (marketplaces, matching theory).

Fall, Spring

031726