Gurobi Connect in Copenhagen
April 3, 2025

Christopher Smith
Finance Account Director, Gurobi Optimization

As the demand for sustainable energy solutions grows, efficient battery storage and optimization play a crucial role in enhancing grid stability and energy efficiency. In this session, Ramboll will present their business case on battery optimization, addressing key operational research (OR) challenges related to energy storage, distribution, and management.

Finding high-quality feasible solutions quickly is a key challenge in real-world optimization applications. Gurobi is equipped with a rich set of heuristics that enable rapid solution generation and improvement to strike the balance between time-to-good-solutions and proof-of-quality. This talk will explore heuristics as an integral part of Gurobi’s solving strategy and demonstrate how certain non-default settings can drastically reduce the time-to-first-solution on certain models. Join us to discover how Gurobi makes advanced mathematical optimization a practical and effective tool for real-world decision-making.

Lennart Lahrs
Technical Account Manager, Gurobi Optimization

As a bank, we handle millions of transactions a day. Almost all of these (>99.99%) are completely fine and legitimate transactions. However, every once in a while a sanctioned entity is either trying to send or receive money with a payment we process. We want, and are legally required, to stop such payments. Due to the high volume of transactions a day, algorithms play a crucial role in flagging potentially suspicious payments. If any of these algorithms judge there to be a match to a sanctioned entry, the payment is investigated manually.

This poses a challenge though: the number of true positives is very low, so even the best algorithms will mostly flag false positives (>95%). This means almost all of the payments an analyst goes through are fine, they just have an unlucky similarity to a sanctioned entity. To reduce this issue, the algorithmic thresholds have to be as tight as possible, while still finding all the true positives.

In our presentation, we will show how this problem can be posed as a mixed-integer programming problem and the results it yields. We also discuss how to extend the formulation to allow for a risk-based approach, where a certain number of misses of true positives are allowed, in order to further reduce the number of false positives. This results in a fascinating pareto front, which is used as a basis of discussion around the price of catching all true positives.

Richard Oberdieck,

Lead Data Scientist, Banking Circle

Christopher Smith,

Finance Account Director, Gurobi Optimization