Optimization Models for Logistics, Networks, and Markets

Catalog description

Hands-on experience with integer linear programming and dynamic programming: creating ILPs and DPs, implementing them, critiquing them, understanding solver output, and improving ILPs using better variables, constraints, symmetry breaking, etc. Examples of problems that we will study in this course are logistical problems like sequencing in production, scheduling problems with conflicts (vertex coloring), matching problems for markets and clustering problems in networks, but are not limited to these domains. In addition, a variety of general linear programming techniques such as Fourier-Motzkin elimination, Dantzig-Wolfe decomposition, Benders decomposition and extended formulations may be covered, as well as rounding techniques of LP solutions.