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Please come with plenty of time to get settled. Coffee, tea and the Gurobi team will be waiting to welcome you and get you set up for the day.
Join Duke Perrucci, CEO of Gurobi Optimization, as he kicks off the summit with an inspiring keynote on the transformative power of mathematical optimization. In a world increasingly driven by complex decisions and data, optimization has become a critical tool for organizations striving to operate smarter, faster, and more efficiently.
Duke will share his vision for the future of decision intelligence, Gurobi’s role in shaping that future, and how innovation and customer collaboration continue to fuel the company’s growth.
Join Oliver Bastert, Gurobi’s Chief Technology Officer, for an inside look at the Gurobi product roadmap. He’ll share what’s new, what’s coming next, and how Gurobi continues to push the boundaries of solver performance and innovation. Learn how customer feedback, emerging technologies, and industry trends are shaping the future of our optimization engine.
Efficient planning and cost-effective rollout of fiber optic networks are major challenges for infrastructure providers and municipalities. At A1, we are using a fully automated FTTH planning tool to define expansion areas directly within the GIS system and calculate reliable preliminary designs, including detailed cost reports, within minutes.
This approach allows us to calculate hundreds of networks and various expansion strategies in parallel which we then prioritize based on ROI and revenue forecasts. The result: greater transparency in decision-making, significant time and cost savings, and maximum planning confidence for all stakeholders in fiber network deployment.
Enjoy your hot or cold drink while connecting with fellow attendees. A perfect moment to recharge, network and connect with Gurobi’s partners visiting our Partner Exhibition.
Join Ronald van der Velden, Manager of Technical Account Management – EMEA, and Kostja Siefen, Senior Director of Technical Account Management, for an inspiring look at what it means to Empower Bold Decisions with Gurobi. Discover how technical users are modeling complexity without compromise, solving ambitious challenges, and turning data into action through advanced mathematical optimization. This session will highlight real-world examples, best practices, and the strategic role of technical collaboration in maximizing the value of optimization.
SAP is the global leader in Supply Chain Management Software, offering a wide array of cloud and on-premise solutions for supply chain planning, logistics, manufacturing, product lifecycle management, enterprise asset management, and sustainable supply chains. For over 25 years, optimization algorithms have been a core component of SAP’s Supply Chain solutions.
Applying these algorithms to real-world, large-scale supply chains presents significant functional and performance challenges. To address these, SAP employs a variety of optimization algorithms tailored to the complexity of the problem, often combined with (meta-)heuristics to enhance scalability and performance.
This talk provides an overview of the algorithms used in different solution areas and highlights the challenges in terms of scope, data volume, and scalability of real-world planning problems faced by SAP customers.
Take a break to enjoy lunch, connect with peers, speakers and the Gurobi team and recharge for the afternoon sessions. Don’t forget to visit our Partner Exhibition to learn how our partners can support your optimization projects.
Learn how Empowering Bold Decisions through optimization and decision intelligence can reshape your organization’s strategy, operations, and competitive advantage.
Gain insights from real-world success stories, discover how bold data-driven actions drive better outcomes, and explore cutting-edge strategies that set industry leaders apart.
Key takeaways:
Dive deep into the art and science of optimization modeling — where Empowering Bold Decisionsmeans solving complexity without compromise.
Discover how Gurobi users push the boundaries of what’s possible, model intricate challenges, and unlock new solution spaces to power smarter strategies and innovation.
Key takeaways:
Current communication systems are significantly impacted when network flow demands exceed available resource capacities. Traditional prioritization mechanisms aimed at ensuring quality of service often become ineffective under such high congestion levels. This challenge is further compounded in radio link networks (e.g., satellite links), where congestion may arise due to exogenous factors such as rain, fog, sandstorms, or solar activity.
To ensure that priority traffic is delivered reliably — while respecting the specific constraints of individual network flows— we propose a mathematical optimization-based approach. In this talk, we will present the formulation of the underlying model, the rationale behind its design choices, and the operational constraints, particularly w.r.t. execution time. We will also demonstrate how Gurobi is able to solve these models in milliseconds, enabling real-time deployment in industrial settings.
Finally, we will showcase a live demonstrator using real network equipment and the decision engine built on the techniques discussed in this presentation.
Germany currently operates as a single electricity bidding zone (shared with Luxembourg), creating a “copper plate” illusion: electricity is priced uniformly across regions, regardless of physical grid constraints. However, rising north‑south congestion – driven by high renewable generation in the north and heavy industry in the south – has led to high redispatch costs (1.3 to 3.2 b€ between 2019 and 2023) and inefficiencies.
Supported by ENTSO-E’s Bidding Zone Review, the debate on more granular pricing – such as smaller bidding zones or nodal pricing – is gaining traction. According to Agora’s new study improved coordination of electricity generation, consumption, and grid usage through enhanced locational price signals could have increased grid stability and saved up to €1.2 billion in 2023 alone. To inform the debate and as the first live web tool, the “Locational Agorameter” publishes hourly locational prices across Germany in real time. A proposed roadmap, coordinated at the European level, outlines pathways toward a locally differentiated pricing system. Since electricity consumers overall would benefit from locational prices, the roadmap outlines steps toward locally differentiated pricing, including compensation measures for affected stakeholders such as renewable producers in the north and energy-intensive industries in the south.
The talk concludes with future perspectives – including short-term complementary locational investment signals, options for smaller zonal versus nodal market design, and implications for industrial competitiveness – and invites discussion on whether Germany can balance regional equity with system-wide decarbonization goals.
Gousto is one of the UK’s leading meal kit providers, delivering personalised weekly
menus to millions of homes. Behind every box is a complex web of optimised
decisions—spanning menu planning, demand forecasting, supply chain
management, and ultimately, fulfilment. In this session, we’ll dive into a recent
optimisation challenge within Gousto’s automated factory, where hundreds of fresh
ingredients must be dynamically allocated across picking stations each week. As
menu variety scaled rapidly, our rule-based system was no longer sufficient to
maintain throughput. Faced with a high-dimensional, combinatorial allocation
problem, we partnered with Gurobi experts to develop a robust, efficient
optimisation model—despite non-linear dependencies in our performance metrics.
Using our FactoryTwin simulation platform, we tested and validated the approach
at scale before deploying it into production. This talk will share the technical
journey, the business impact, and how Decision Intelligence is shaping the future of
operations at Gousto.
Efficient personnel scheduling in health care is a complex, dynamic challenge involving diverse roles, preferences, and constraints. This talk presents the smartPEP solution by POLYPOINT, which empowers planners and staff through participatory planning and intelligent optimization to streamline the monthly scheduling process. By integrating staff preferences, contractual agreements, and department-specific requirements, smartPEP enables fair, high-quality schedules while significantly reducing planning effort. The system leverages advanced algorithms—including custom work block generation and integer linear programming—to automate the schedule generation process. A key feature is a modern, web-based planning board that allows planners to launch schedule generation, evaluate plan quality, and make manual adjustments with ease.
Our customers have reported substantial time savings and improved satisfaction among staff and planners alike. This session explores the underlying decision science, showcases real-world feedback, and highlights how smartPEP supports bold, collaborative, and data-driven decisions in health-care workforce management.
The DeepBlue project at Danone is an initiative aimed at optimizing milk sourcing using the Gurobi mathematical solver. The project is part of the end-to-end milk source-to-pay process and involves monthly business case runs and quarterly rolling forecasts. The main challenge addressed by DeepBlue is balancing milk supply and demand, considering factors like cow seasonality, promotion, and consumption seasonality
Levasoft leverages the Gurobi Solver to solve complex optimization challenges in the planning of flat-roof photovoltaic systems – particularly in ballast calculation and inverter configuration.
Since flat-roof systems are not mechanically fixed but held in place by ballast, planning requires precise calculations based on factors such as wind load, building geometry, and engineering reports. Economic parameters like cost and installation speed also play a crucial role. The search space often includes tens of millions of possible combinations. Gurobi delivers optimal results with exceptional performance – and verifies their validity at the same time.
Gurobi also powers the automated selection of inverters. From thousands of technical and financial parameters, it identifies the most efficient configurations in seconds.
Take a break to enjoy lunch, connect with exhibitors, peers, speakers and the Gurobi team and recharge for the afternoon sessions.
The automated machine-based design of next-generation access fixed networks under consideration of detailed real-world geographical data, usable and existing network infrastructure and a variety of user individual network design principles is still a challenging task with respect to the computational complexity. In this context we present the methodology and technical implementation of an according generic network solver approach, which has been developed for the operational use of A1Telekom Austria in order to achieve real world network designs and corresponding investment estimates within user acceptable time spans. We sketch the underlying data as well as the mathematical modeling, which is characterized by a meta-heuristic approach. Hereby the computational power of the Gurobi Optimizer is used in order to solve the underlying combinatorial base models. Hence, we discuss these base models in detail and especially focus on the challenges arising from the complexity of real-world data in providing good quality initial solutions, in managing the descent performance and in handling arising modelling and data conflicts. Moreover, we describe the technical integration of the Gurobi Optimizer by using a middle ware application, which on the one hand supports a context-specific control of the optimizer by the system user and which on the other hand reduces the maintenance effort of the overall system.
Strategic workforce planning in retail must navigate high turnover, operational complexity, and geographic dispersion. This project introduces an optimization model designed to allocate workforce resources efficiently across diverse store formats, regions, and role clusters within a major retail organization.
The model minimizes labor costs and staffing imbalances over a multi-period planning horizon, subject to constraints such as minimum staffing levels, contractual composition, internal mobility limits, and skill requirements. Forecasted voluntary turnover—derived from external predictive models—is incorporated as an exogenous input, enabling the model to proactively rebalance the workforce through hiring, promotions, and internal transfers. In this talk, we will outline the core formulation, explore its deployment using Gurobi’s solver and scalability in a real-world setting, and demonstrate how it integrates human-centric uncertainty into a prescriptive optimization framework.
Artificial Intelligence has become a cornerstone in driving Iberia’s digital transformation, significantly reshaping operations, customer experience, and strategic decision-making. While substantial progress has been made, optimization remains a promising yet underexplored frontier. This talk highlights AI’s pivotal role at Iberia, illustrating transformative impacts across multiple business areas. Specifically, it will delve into how advanced optimization tools, such as commercial solvers like Gurobi, have delivered tangible value by streamlining complex operations and resource allocation challenges. Through concrete examples of successful applications, the session emphasizes that, despite current achievements, there remains significant untapped potential. By embracing optimization-driven strategies, Iberia can continue its trajectory toward operational excellence and industry leadership.
Airlines plan their aircraft and crew schedules using OR methods. However, these schedules are often disrupted due to the irregular nature of flight operations. Airline recovery aims to minimize the cost of these disruptions by adjusting aircraft routes, crew schedules, and passenger itineraries. Due to time constraints, traditional OR methods cannot fully address these issues. This research introduces a novel approach that combines mixed-integer optimization and supervised machine learning to tackle large-scale airline recovery problems more effectively. By analyzing historical disruption patterns, the method adds constraints to the optimization model, significantly narrowing the solution space for faster computation. Computational studies using real-world data from US airlines with over 2,500 daily flights demonstrate that the proposed approach can generate solutions of significantly higher quality than benchmark methods.
When remarkable people share ideas, something powerful happens. This closing panel brings together diverse perspectives to explore big questions that inspire, challenge, and connect us—while also highlighting the key insights and bold ideas shared throughout the Business Track. Through lively discussion and shared reflections, we’ll uncover stories, spark new ways of thinking, and revisit strategies and lessons that can help you drive smarter, bolder decisions. You’ll leave with fresh perspectives, actionable takeaways, and inspiration to create greater impact within your organization long after the session ends.
When the toughest problems land on the table, optimization experts are the ones called to crack them. In this lively panel, you’ll hear from practitioners who took on big, risky projects—and made them work. They’ll share their stories of courage, struggles, and surprising lessons learned along the way. Expect a mix of fun, honesty, and practical tips you can use on your own high-stakes projects.
Enjoy a relaxed setting to continue the day’s conversations, exchange ideas, and build new connections over drinks and light refreshments. Our Speaker Hub will give you the chance to engage directly with all speakers, ask follow-up questions from the sessions, and dive deeper into topics that matter most to you. This is your opportunity to combine networking, knowledge-sharing, and a bit of celebration as we wrap up the day.
Most optimization projects promise business value – but too many never move beyond the model. Why? Because adoption depends on more than math: it’s about trust, operational reality, and clear communication.
The BmO workshop at the Gurobi Decision Intelligence Summit 2025 is designed to bridge this gap. We’ll move beyond theory to tackle the real reasons why optimization fails – or succeeds – in the field:
• How do you translate technical models into outcomes that business leaders understand and value?
• How do you build trust in your data and your recommendations, across silos?
• What makes an optimization tool truly indispensable – and what happens when it’s gone?
• How do you overcome objections, skepticism, or change fatigue?
Through practical exercises, peer learning, and candid discussions, you’ll gain tools and strategies to turn analytics into real business impact.
Whether you’re an optimizer, a business leader, or somewhere in between – BmO is your chance to close the gap between models and results.
Join us to transform optimization from an academic exercise into an engine for operational excellence and real-world value.
Significant progress in optimization software can be facilitated by advances in algorithms or computing environments. 2025 has been a particularly exciting year in optimization due to advances in both. Graphical Processor Units (GPUs) have been very successful in the computations associated with Large Languages Models (LLMs), raising the question of whether they provide similar performance
boosts to optimization algorithms. GPUs can perform very simple calculations with massive parallelism.
However, most of the computations in the simplex methods, barrier algorithm, and branch and bound algorithm are more complex. Thus, attaining massive speed ups in GPUs for these methods is more complicated than simply recompiling existing implementations on a machine with GPU. Fortunately, the recent appearance of the Primal-Dual Hybrid Gradient algorithm (PDHG) illustrates the synergy between hardware and software that yields potentially larger speedups. PDHG relies on only the simple computations upon which GPUs thrive.
This presentation will provide a preview into version 13 of Gurobi, scheduled for November of 2025. This will include an update on development efforts on PDHG, both on CPUs and GPUs. We will also discuss advances in the global MINLP solver, including support for additional modeling constructs, improved heuristics, development efforts on a dedicated local nonlinear solver, and other performance improvements.
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.
In this session we explore some of Gurobi’s advanced features through the lens of solution explainability, including solution pools, infeasibility analysis, and multi-scenario analysis. Explainability in practice requires both technical skills and successful communication with stakeholders who don’t know how Gurobi works (and that’s ok!). We show how these tools are not just for modeling – they can help users interpret optimization results and build trust their solutions.
Most optimization projects promise business value – but too many never move beyond the model. Why? Because adoption depends on more than math: it’s about trust, operational reality, and clear communication.
The BmO workshop at the Gurobi Decision Intelligence Summit 2025 is designed to bridge this gap. We’ll move beyond theory to tackle the real reasons why optimization fails – or succeeds – in the field:
• How do you translate technical models into outcomes that business leaders understand and value?
• How do you build trust in your data and your recommendations, across silos?
• What makes an optimization tool truly indispensable – and what happens when it’s gone?
• How do you overcome objections, skepticism, or change fatigue?
Through practical exercises, peer learning, and candid discussions, you’ll gain tools and strategies to turn analytics into real business impact.
Whether you’re an optimizer, a business leader, or somewhere in between – BmO is your chance to close the gap between models and results.
Join us to transform optimization from an academic exercise into an engine for operational excellence and real-world value.
This presentation offers a unique opportunity to witness live optimization model tuning by Gurobi Experts. We will demonstrate real-time parameter adjustments, showcasing the step-by-step process and strategies employed to enhance performance. Attendees will gain valuable insights into identifying key performance factors and mastering parameter tuning techniques for optimized results. If you have a model you’d like to see tuned during the session, please reach out to us at support@gurobi.com for instructions to send us your model.
Take a break to enjoy lunch, connect with peers, speakers and the Gurobi team and recharge for the afternoon sessions.
In addition to solving LPs, MIPs and MIQCPs, Gurobi has many useful features that you may not be aware of. In this talk we review modeling features such as multiple objectives, multiple scenarios, solution pools and general constraints. We also present features to help analyzing infeasibility and tools designed to analyze and improve the performance of Gurobi on your models.
In this session, we will familiarize users with LP and MIP optimization models. In the first part, we will explain how a generic optimization model differs from an LP, give a geometric interpretation to a specific LP instance, and give high-level (geometric) ideas behind the simplex and interior-point algorithms for solving LP. In the second part, besides presenting a specific and simple MIP model, we will demonstrate basic techniques for solving MIP. Specifically, we will exemplify branch-and-cut framework, demonstrate forming bounds on MIP, presolve, cutting planes, heuristics, and discuss termination criteria. The session will conclude with basics of log-file reading and interpretation.
Explore how to use Large Language Models (LLM) to find ideas, develop models, and write code.
Gurobi has a ScaleFlag parameter that can help improve performance on numerically unruly models. However, in some cases using this parameter treats the symptom of the problem rather than the root cause.
Better performance, both regarding run time and solution quality, may be obtained by considering the best scaling choices during the model formulation process. After discussing suitable background information, this presentation will consider scaling strategies for model creation, including proper choice of units of measurement and alternate formulations to preempt numerical problems.
Join this engaging panel moderated by Silvana, Director of Product Marketing, as Gurobi’s brightest minds—Sonja Mars, VP of Support, Oliver Bastert, Chief Technical Officer and Xavier Nodet, Senior Product Manager—come together to share their perspectives. Hear directly from our technical leaders as they discuss challenges, innovations, and the future of optimization, offering unique insights into how Gurobi continues to push the boundaries of what’s possible.
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Gurobi Summit EMEAI 2025
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