EURO 2024

European Conference on Operational Research

Technical University of Denmark DTU, Lyngby

The European Conference on Operational Research (EURO) invites you to Denmark, to share your experience with optimization and planning.

EURO 2024 is the 33rd edition of the largest European conference on operations research. Academics and practitioners from all over the world will attend the conference and share their latest findings.

The conference gives not only the possibility to be updated with the latest advances in optimization techniques; it is also the largest European networking events for optimization experts.

The conference features sessions and networking meetings from all the major European research working groups. It also features a separate stream for practitioners, where the application of optimization is showcased with real-life examples.

GAMS will attend the conference with a booth and interesting speeches about GAMSPy. Visit us and do not miss out our talks in Copenhagen.

GAMSPy: The Best of Both Worlds - Integrating Python and GAMS

By Justine Broihan

Optimization applications combine technology and expertise from many different areas, including model-building, algorithms, and data-handling. Often, the gathering, pre/post-processing, and visualization of the data is done by a diverse organization-spanning group that shares a common bond: their skill in and appreciation for Python and the vast array of available packages it provides. For this reason, GAMS offers a new comfortable way to integrate with Python on the data-handling and modeling side. In this talk, we will explore the benefits of our Python library GAMSPy.

Integrating Machine Learning with GAMSPy

By Hamdi Burak Usul

GAMSPy is a powerful mathematical optimization package which integrates Python’s flexibility with GAMS’s modeling performance. This combination opens doors to previously challenging applications, notably in bridging the worlds of machine learning (ML) and mathematical modeling. While GAMS excels in indexed algebra, ML predominantly relies on matrix operations. To enable applications in ML, our work introduces essential ML operations such as matrix multiplications, transpositions, and norms into GAMSPy. In this talk, we showcase the use of these additions by generating adversarial images for an optical character recognition network using GAMSPy. We highlight GAMSPy’s versatility and its potential to be used in ML research and development. We delve into future prospects, show how GAMSPy’s approach differs from existing alternatives and discuss innovative methods where mathematical modeling intersects with machine learning.

GAMS Engine SaaS: A Cloud-Based Solution for Large-Scale Optimization Problems

By Frederik Proske

GAMS Engine SaaS is a cloud-based service that allows users to run GAMS jobs on a scalable and flexible infrastructure, currently provided by Amazon Web Services (AWS). It was launched in early 2022 and has since attracted a variety of customers who benefit from its features, such as horizontal auto-scaling, instance sizing, zero maintenance, and simplified license handling. GAMS Engine SaaS is especially suitable for workloads that require large amounts of compute power and can be adapted to many different scenarios. In this presentation, we show a case study of a large international consultant agency that uses GAMS Engine SaaS to run Monte-Carlo simulations of a large energy system model in response to varying climate change scenarios. We describe how they leverage the GAMS Engine API to submit and monitor their jobs, how they select the appropriate instance type for each job, and how they can use custom non-GAMS code on Engine SaaS. We also discuss the challenges and benefits of using GAMS Engine SaaS for this type of application, and provide some insights into the future development of the service.