The annual conference of the Society for Operations Research (GOR e.V.) took place in Bielefeld from September 2–5, 2025, hosted by Bielefeld University. This year’s theme, “Operations Research in a Complex World,” brought together participants from academia and industry to discuss ongoing developments in the field.
The GAMS team was present with several contributions and enjoyed connecting with colleagues and users. While the program offered a wide range of topics, for us the highlight was the opportunity to present and discuss our own work with the OR community.
We are happy to share our abstracts and presentation slides once again here, so that those who could not attend—or would like a second look—can dive deeper into our contributions.
Our thanks go to the organizers, speakers, and participants for their efforts in putting together this year’s GOR meeting. We look forward to continuing the exchange of ideas and to next year’s conference.
The abstracts:
Pre-Conference Workshop
An Introduction to Modelling with GAMSPy
Workshop Organizers: Frederik Fiand & Lutz Westermann
This 90-minute workshop offers a hands-on introduction to GAMSPy. GAMSPy combines the high-performance GAMS execution system with the flexible Python language, creating a powerful mathematical optimization package. It acts as a bridge between the expressive Python language and the robust GAMS system, allowing you to create complex mathematical models effortlessly.
Join us to explore GAMSPy’s fundamental functionalities through practical, interactive exercises. We’ll cover everything from defining sets, parameters, variables, and equations to solving models and retrieving results, all within a familiar Python environment. Beyond the basics, we’ll also provide a glimpse into more advanced features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.
Whether you’re a seasoned GAMS user looking to integrate with Python or a Python user curious about optimization, this workshop will equip you with the essential skills to get started with GAMSPy.
Our GAMS presentations:
Embedding Neural Networks into Optimization Models with GAMSPy
Authors: Frederik Fiand, Michael, Bussieck, 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.
A Whole New Look for CONOPT
Authors: Lutz Westermann, Michael Bussieck
Following GAMS’ recent acquisition of CONOPT from ARKI Consulting & Development A/S, this presentation delves into the continuous evolution of this robust nonlinear optimization solver, emphasizing the advancements introduced in the latest release and the strategic implications of the new ownership.
The latest iteration of CONOPT introduces new APIs, e.g, for C++ and Python, opening up new possibilities for a clean, efficient, and robust integration into various software environments and projects requiring nonlinear optimization.
Finally, we will demonstrate the practical application of providing derivatives to CONOPT, an important step that is often necessary to achieve the best possible performance.
Check our presentation slides for more information:
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