The EURO 2025 conference brought us to Leeds this year, where the GAMS team once again had the opportunity to connect with the European Operations Research community. From Sunday to Wednesday, our team — Justine Broihan, Andre Schnabel, Muhammet Soytürk, and Frederik Proske — represented GAMS at our booth and through a series of presentations. As additional supporters to our team, our colleagues Stefan Vigerske and Stephen Mayer also went to Leeds to have two talks during the conference, contributing valuable insights from their areas.
Our booth was buzzing — so much so that we ran out of flyers and merchandise by the end of day one.
Connections, Conversations & Community
The Sunday evening social event — conveniently hosted near our booth — was a great chance to mingle in a more relaxed setting. While day two brought fewer people than day one, the conversations were more in-depth and often very promising. As expected, day three saw a quieter crowd, but still valuable interactions.
It was surprising to notice how, in contrast to the 2024 edition, this year’s EURO conference featured significantly less participation from the enterprise sector. As a result, the event leaned much more heavily toward academic discussions, giving it a distinctly research-focused atmosphere.
Talks, Recognition & Global Reach
Each GAMS team member gave a talk during the conference, all of which were well received. In fact, GAMS even made its way into other presentations — highlighting the growing visibility of our tools within the OR community.
EURO 2025 was another milestone for GAMS, confirming the growing traction of GAMSPy and our broader ecosystem within the academic and OR communities. A huge thank-you goes to Andre, Muhammet, Frederik, and Justine for their energy, dedication, and teamwork.
We’re already looking forward to the next one—see you at EURO 2026! 🌍
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Our Abstracts
From Chaos to Clarity: Consulting Lessons from Optimizing Alpro’s Energy and Production Scheduling
By Justine Broihan
Every optimization model in production tells a deeper story—of translating theory into practice through close collaboration and iterative problem-solving. In this talk, we share the consulting journey behind designing and implementing a GAMS-based decision-support system at Alpro—a leader in plant-based food production. Far beyond just a technical deployment, this project was a masterclass in managing complexity, aligning stakeholders, and building trust.
We’ll explore the real-world challenges faced by our consulting team: translating on-site planner needs into mathematical logic, managing the messiness of live operational data, and navigating the cultural shift toward automation and dynamic market participation. Through a structured but adaptive process—iterative prototyping and continuous feedback loops—we helped Alpro optimize energy production and conditional bidding on the day-ahead electricity market.
Whether you’re an academic researcher, operations leader, or consultant, this talk gives a candid look at what it takes to bridge the gap between sophisticated models and messy real-world implementation.
Embedding neural networks into optimization models with GAMSPy
By Andre Schnabel
GAMSPy is a powerful mathematical optimization package which integrates Python’s flexibility with GAMS’s modeling performance. Python features many widely used packages to specify, train, and use machine learning (ML) models like neural networks. GAMSPy bridges the gap between ML and conventional mathematical modeling by providing helper classes for many commonly used neural network layer formulations and activation functions. These allow a compact description of the network architecture that gets automatically reformulated into model expressions for the GAMSPy model.
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.
GAMSPy - A Glue Between High Performance Optimization and Convenience
By Muhammet Soytürk
A typical optimization pipeline consists of many tasks such as mathematical modeling, data processing, and data visualization. While GAMS has been providing tools with great performance for mathematical modeling, Python and its giant ecosystem provide packages for data gathering, pre/post-processing of the data, the visualization of the data and developing necessary algorithms by utilizing existing ones. In this talk, we will talk about a “glue” package GAMSPy that aims to combine these two environments to leverage the best of both worlds.
A parallelisation framework for solving challenging integrated long-haul and local vehicle routing problems
By Stephen Mayer
The integrated long-haul and local vehicle routing problem with an adaptive transportation network is a very challenging optimisation problem. The adaptive nature of the transportation network means that the resulting optimisation problem is extremely large and difficult to solve directly using general purpose solvers. As such, the best approach for finding high quality solutions is to use heuristics combined with a branch-and-bound algorithm. Our research has developed a parallelisation framework that concurrently executes heuristic and exact approached to find high-quality solutions to the integrated long-haul and local vehicle routing problem. Within the parallelisation framework we have attempted to solve the complete problem directly using a MIP solver and by applying Benders’ decomposition. The results will show that the use of parallelisation and applying Benders’ decomposition increases the scale of problems that can solved and improves the upper and lower bounds that can be achieved.
The SCIP Optimization Suite 10
By Stefan Vigerske
In this year, the SCIP Optimization Suite reaches its first double-digit major version number. Starting with an algebraic modeling language, a simplex solver, and a constraint integer programming framework, containing the world’s best non-commercial mixed-integer programming solver, it has evolved over the last 20+ years into a swiss army knife for anything where relaxations are subdivided, trimmed, generated dynamically, and eventually solved, be it on embedded, ordinary, or super-computers. The newest iteration brings major updates for the presolving library PaPILO, the generic decomposition solver GCG, and the branch-cut-and-price framework SCIP itself. In this talk, we will give a short overview on the current SCIP Optimization Suite ecosystem and catch a glimpse on the new features contributed by over 15 developers in the newest major release.