You have probably been there: booking that dream vacation or registering for a conference abroad. You mindlessly add a flight, a small hurdle before reaching your destination. As you get comfortable in your seat among hundreds of fellow passengers, you glance out the window and see dozens of planes gliding around the airport. Have you ever stopped to wonder what it takes to make this happen? Behind the scenes, airlines face a logistical puzzle unlike any other. How do they get you and thousands of others to the right place at the right time with the right resources at an acceptable cost? From developing flight schedules years in advance to real-time execution on the day of travel, optimization is key.

Join us to explore how KLM Royal Dutch Airlines, one of Europe's leading carriers, uses advanced optimization techniques to prepare and adjust flight schedules. Discover how we optimize the deeply interconnected planning and allocation of resources like aircraft, crew, technicians, and ground handlers, ensuring everything works in harmony.

But the planning is just the start. When faced with disruptions such as bad weather or aircraft breakdowns, you will learn how airlines use real-time optimization to bring order to chaos. Be prepared to be surprised by the hidden world of mathematics that keeps you flying smoothly. You might even leave with a newfound appreciation for that occasional (and sometimes mysterious) schedule change!

This session is perfect for anyone who has ever stepped on a plane and for those curious about the fascinating intersection of business, technology, and that incredible feeling of soaring through the sky.

This session builds on the foundational concepts discussed in the previous talks, shifting focus to the practical implementation of mathematical optimization across key business areas to ensure transformation and resilience.

We will explore real-world applications and strategies that have proven effective in addressing the challenges associated with major global trends such as energy transition, supply chain resilience, and digitalization.

This session builds on the foundational concepts discussed in the previous talks, shifting focus to the practical implementation of mathematical optimization across key business areas to ensure transformation and resilience. We will explore real-world applications and strategies that have proven effective in addressing the challenges associated with major global trends such as energy transition, supply chain resilience, and digitalization.

The discussion will highlight:

• Best Practices: Analyzing successful implementations of optimization in diverse industries, detailing the challenges faced and the solutions employed.
• Strategic Planning: Outlining how businesses can integrate optimization into their strategic planning processes to better adapt to changing market conditions and regulatory landscapes.
• Technology and Tools: Examining the essential concepts of Gurobi Optimizer, that facilitate effective optimization, and discussing how to choose the right ones based on specific business needs.
• Skill Development and Culture Shift: Addressing the human element, focusing on developing the necessary skills within teams and fostering a culture that embraces data-driven decision-making.

Participants will leave with a clearer understanding of how to practically apply mathematical optimization techniques in their operations, equipped with actionable insights and guidelines to initiate or enhance their optimization projects.

Discover how BAMA, Norway’s leading fruit and vegetable supplier, automized its transport planning with a cutting-edge and award-winning optimization model powered by Gurobi. Learn how we developed and implemented a custom Mixed Integer Linear Programming model to maximize transport efficiency, reduce costs and CO2 emissions, and ensure product freshness.

Our project leverages Azure services, including Storage containers, Batch, Functions, and Key Vault, seamlessly integrated with GitHub for version control and Azure Pipelines for CI/CD, all managed through Terraform.

Join us as we share our 360-degree view on the journey from manual processes to a sophisticated, data-driven approach: we will delve into the technical architecture, challenges, and achievements, providing valuable insights for organizations aiming to enhance their transport logistics with advanced technology.

Providing reliable delivery dates for customer orders efficiently is vital for maintaining healthy customer relationships and upholding the company's value proposition. The cork composites manufacturer in this study faced challenges in analyzing customer orders due to the difficulty of accurately and efficiently simulating existing production orders within their multi-unit production environment. To address this issue, an optimization model was developed that enables the Production Planning department to provide accurate delivery dates for each customer order. It also allows for what-if analyses regarding changes to the existing production based on the commercial priority of each order and offers an overview of potential bottlenecks in the industrial areas for the coming months.

In this talk, we will present the process of building this model, the results obtained, and the challenges encountered in modeling a multi-stage production process within a multi-unit industrial environment, as well as the process designed to facilitate its adoption.

In today's data-driven retail landscape, Jumbo Supermarkten is at the forefront of innovation by seamlessly integrating mathematical optimization models across critical business functions. This presentation explores how Jumbo has enhanced floor space management, optimized product assortment, fine-tuned pricing strategies, and streamlined logistics operations through sophisticated optimization techniques. Building on the foundation of successful machine learning projects, these optimization models allow Jumbo to become even more relevant for customers and improve the operational efficiency at the same time.

Join us to discover how machine learning and mathematical optimization have become an integral part of Jumbo’s business decisions. With this combination, we transform data into actionable recommendations and empower data-informed decision-making.

Optimization is often seen as a black-box: once your model is handed over to Gurobi, you just trust it to come back with the optimal result.

Knowing what's going on within that black box will help you understand how changes in your model formulation, input data or solver parameters may impact performance of Gurobi. In this session we have a look under the hood to see which algorithms are employed to solve your problems.

This session builds on the foundational concepts discussed in the previous talks, shifting focus to the practical implementation of mathematical optimization across key business areas to ensure transformation and resilience. We will explore real-world applications and strategies that have proven effective in addressing the challenges associated with major global trends such as energy transition, supply chain resilience, and digitalization.

The discussion will highlight:

• Best Practices: Analyzing successful implementations of optimization in diverse industries, detailing the challenges faced and the solutions employed.
• Strategic Planning: Outlining how businesses can integrate optimization into their strategic planning processes to better adapt to changing market conditions and regulatory landscapes.
• Technology and Tools: Examining the essential concepts of Gurobi Optimizer, that facilitate effective optimization, and discussing how to choose the right ones based on specific business needs.
• Skill Development and Culture Shift: Addressing the human element, focusing on developing the necessary skills within teams and fostering a culture that embraces data-driven decision-making.

Participants will leave with a clearer understanding of how to practically apply mathematical optimization techniques in their operations, equipped with actionable insights and guidelines to initiate or enhance their optimization projects.

The Spanish electricity market offers various tariffs to the users. The configuration of the best supply option often involves a complicated set of decisions, since the final price is highly conditioned by the market price volatility and consumption stochasticity. In this talk, we will present OIIO, an innovative mobile application that determines the optimal tariff for SMEs consumers. Using algorithms that combine machine learning and optimization, OIIO makes decisions under uncertainty to minimize the cost of the electric bill, putting this power in the hands of everyone through a simple app. We will focus on the problem of deciding the power to contract according to the billing precepts of the power term and the restrictions of the electricity marketers.

In today's dynamic and highly competitive business environment, where uncertainty is greater than ever, making informed and optimized decisions is crucial for organizational success.

An increasing number of companies are investing in optimization-based software solutions to enhance efficiency, increase customer satisfaction, and achieve sustainability. Many companies realize that shifting the focus from planning to optimized decision-making unlocks maximum business benefits. In this talk, we will discuss our decision-centric optimization experience in various industries and the complementary power of the cutting-edge decision intelligence technologies of ICRON and Gurobi. In particular, we will explore how ICRON addresses common business requirements, such as balancing multiple competing objectives and conducting rapid scenario analysis by leveraging Gurobi features like lexicographic optimization and warm start. We will also discuss how ICRON integrates with Gurobi in various deployment scenarios, including on-premises and public cloud environments.

In this session we will guide you through the integration of Gurobi with the greater Python ecosystem. We will demonstrate model building patterns based on NumPy and SciPy.sparse data structures and how to take advantage of indexed DataFrames and Series in pandas. Further we will show how to use trained regressors from scikit-learn as constraints in optimization models. Join us as we delve into the world of optimization with Gurobi and elevate your Python-based workflows.