We are excited to announce that GAMS will be participating in the 34th European Conference on Operational Research (EURO 2025) as both a sponsor and exhibitor. This landmark event, celebrating the 50th Anniversary of the Association of European Operational Research Societies (EURO), will take place from June 22 to June 25, 2025, at the University of Leeds in the United Kingdom.
As a leading provider of high-performance optimization software, GAMS is committed to advancing the field of operational research. Our team will be present at our exhibition booth, where attendees can explore our latest solutions, engage in discussions, and discover how GAMS can support their research and operational needs.
In addition to our exhibition presence, GAMS will contribute to the conference program with several insightful presentations. Our experts will share their knowledge on various topics, showcasing the versatility and power of our software in addressing complex operational challenges.
We look forward to connecting with researchers, practitioners, and students at EURO 2025.
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Do not miss on our insightful presentations in Leeds:
Justine Broihan, Frederik Fiand, Robin Schuchmann
As global demand for plant-based foods continues to rise, manufacturers like Alpro face growing complexity in managing production scheduling, coordinating distribution, and controlling energy costs. This talk provides a practical account of how Alpro uses a GAMS-based decision-support framework to streamline daily operations of their energy production as well as optimizing conditional bidding on the day-ahead market. Attendees will learn how this system schedules producing and consuming assets and strategically purchases or sells energy based on forecasted consumption and spot prices. We discuss the workflow—from data gathering and validation to building trust through iterative prototyping with on-site planners—highlighting how automated scheduling and dynamic market participation can reduce costs and increase operational resilience.
Muhammet Abdullah Soyturk
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.
André Schnabel, Hamdi Burak Usul
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.
In this talk, we demonstrate how GAMSPy can seamlessly embed a pretrained neural network into an optimization model. We also explore the utility of GAMSPy’s automated reformulations for neural networks in various applications, such as adversarial input generation, model verification, customized training, and leveraging predictive capabilities within optimization models.
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.
Stephen Maher
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.
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.
