This session is co-presented by: Moses Thangasamy and Tim Regitz

ALDI Australia’s hard-discount retail model has been built on cost leadership, operational discipline, and highly optimized processes. This session shows how ALDI’s Data Analytics team was built using an optimization-centric approach to deliver not only insights into business performance, but also the realisation of those benefits across the whole organization.  

Gurobi is set up as a key analytical capability at the heart of this team, and the presentation explores how mathematical optimization is applied across high-value supply-chain programmes, including truck routing, warehouse organization, and pallet building.  

The session takes a deeper look at how mixed-integer programming helps create stable, better-packed pallets. To conclude, we discuss the key success criteria at ALDI Australia—how to combine clean data foundations, business-facing analytics teams, and close operational engagement to move beyond insight and deliver measurable business value while remaining a lean, cost-effective hard-discount organisation. 

Presented by:

Kostja Siefen, Gurobi

Ronald van der Velden, Gurobi

Since the mid 2000s the Dutch Railways has worked on and used algorithmic rolling stock plan optimization. Over the years the MIP-based algorithm has evolved from a PhD project into a legacy software product. The solver algorithm itself has stood the test of time and is now being migrated into a new rolling stock planning web application. This is foremost a tale about solving a challenging optimization problem. However, it is also a tale about the challenges of turning an optimization algorithm into a product. A product that is not just adopted, but also appreciated. A product that can survive evolving business requirements as well as a changing IT-landscape over time. I will end with some insights and lessons learned. After all, the successful optimization tools you are building today will be your legacy tools of the future. 

In just a few years, Ryanair has gone from a single optimization model to a portfolio of 10+ products shaping decisions across network, crew, operations, and commercial. This talk is a candid look at how we got here — and what it really took. We will share the challenges that defined the journey: building models that survive operational reality, productising solvers into tools people trust, winning over stakeholders who have run the airline successfully for decades, and scaling a team to deliver at Ryanair's pace. Expect lessons, missteps, and the principles that made optimization stick at the heart of Europe's largest airline.

In this session, we will present how Repsol approaches mathematical optimization at scale through Repsolver, our internal optimization product, built as part of RAIP, Repsol Artificial Intelligence Products. Over the last seven years, Repsolver has become a key capability for developing, industrializing, and reusing optimization solutions across different business areas, with Gurobi as one of the core technologies supporting this journey. 

We will explain our product-based approach to optimization: how we move from individual business problems to reusable optimization components, common data structures, scalable architectures, and deployment patterns that can be applied across multiple domains. As a concrete example, we will present a multimodal, multi-period transportation optimization problem initially developed for LPG, and show how the same Repsolver foundations have been extended and reused for other products, such as asphalts, within Repsol’s Customer Division. 

The session will also explore the future evolution of Repsolver and the role of Generative AI in making optimization more accessible, explainable, and actionable for business users. We will discuss how agentic AI can help users better understand optimization results, explore scenarios, interact with models in a more natural way, and receive decision support that connects advanced optimization capabilities with the real needs of end customers and decision-makers. 

In today’s feed and petfood industry, businesses face increasing complexity driven by volatile raw material markets, stringent nutritional requirements, and frequent supply chain disruptions. Traditional formulation approaches are no longer sufficient to address these multidimensional challenges. This presentation explores how advanced optimization techniques move beyond recipe formulation to support strategic decision-making across the entire value chain. By leveraging powerful solvers, companies can simultaneously balance cost, nutrition, production constraints, and supply uncertainties, enabling more resilient and profitable operations. Real-world examples will demonstrate how advanced optimization helps organizations respond dynamically to disruptions, uncover hidden value, and transform constraints into competitive advantages. Attendees will gain insights into how shifting from formulation-focused thinking to a holistic, strategy-driven approach unlocks new opportunities for efficiency, agility, and business growth in an increasingly complex and unpredictable market environment.  

Lloyd's of London has underwritten risk for over 350 years — from the hulls of sailing ships to today's complex specialty lines. This talk begins with a practitioner's introduction to specialty insurance: how risk is assessed, priced, and assembled into a portfolio at Lloyd's, and why optimizing that portfolio under tail-risk constraints is both commercially critical and computationally hard. It will show how modern optimization enables insurers to make transparent, robust, and trusted decisions in the face of uncertainty and extreme events. 

We then present a new algorithm that bridges actuarial practice and modern optimization theory. The key insight is that the Euler capital-allocation vector — a quantity insurers already compute to attribute risk capital across lines of business — can be repurposed as a cutting-plane constraint, reducing portfolio optimization from a problem that scales with the number of loss scenarios to one that scales only with the number of lines. The result is a significant speedup with no loss in solution quality 

For almost three decades, SAP has been embedding mathematical optimization at the heart of enterprise decision-making, powering some of the world's most complex supply chain and operational planning challenges at scale. 

In this presentation, we discuss how SAP Optimization leverages new technologies in combination with deep business and operations research expertise, to push the boundaries of intelligent, autonomous decision-making in the enterprise. 

CEZ is a large central-European power utility with extensive activities in the generation, distribution, trade in, and sales of electricity and heat, trade in and sales of natural gas, and coal extraction. CEZ uses mixed-integer programming in its planning and dispatch systems, from intraday dispatch to year-ahead planning, from the allocation of ancillary services to a cascade of hydropower plants to scheduling power-plant outages. We present an overview of some of the use-cases, focussing on custom decompositions for mixed-integer programming and deployment in on-premise cloud infrastructure. 

For decades, television advertising inventory has been planned using a combination of business rules, heuristics, and experienced planners. Recently, we introduced mixed-integer programming to tackle some of the same planning challenges from a fundamentally different perspective. The optimization worked, but the broader insights gained during the process were just as compelling. This presentation is not a theoretical discussion about optimization. It is a practical story about what happened when decades of accumulated industry knowledge met modern Operations Research. We will present how optimization helps to not only improve the future, but also learn from the past. 

Supply chain design requires organizations to make high-impact decisions under significant uncertainty, from future demand and capacity needs to regulatory approvals, product launches, and network evolution.

This presentation introduces GLAM, an AI-assisted scenario-planning platform developed to support strategic production network decision-making in the pharmaceutical industry.

At its core, GLAM uses Gurobi-powered optimization to evaluate alternative future scenarios and identify robust production network choices. By combining business-driven scenario creation with mathematical optimization, GLAM helps turn complex planning “what-if” questions into structured analyses that can be assessed consistently across long-term outlooks. We are now exploring how GenAI can further accelerate this process by supporting faster creation and iteration of scenarios.

The presentation will focus on this intersection of GenAI and Gurobi: how AI can help make scenario exploration faster and more accessible, while optimization provides the decision quality needed to evaluate trade-offs and support confident strategic decisions in a complex supply chain environment.