Many real-world applications require making the best possible decisions under complex constraints — whether in scheduling, resource allocation, routing, or planning. These problems quickly become difficult as the number of interacting choices grows.

This session introduces mathematical optimization as a practical tool for solving such problems. Using Gurobi, we demonstrate how to formulate decision problems and compute solutions that satisfy all constraints and come with clear guarantees about their quality.

You’ll see how to express optimization models using familiar data structures such as NumPy arrays, SciPy.sparse matrices, and pandas DataFrames.

By the end of the session, you’ll have an understanding of how to approach modeling and solving complex decision problems — and how optimization can be used to support reliable, data-driven decisions.