Mathematical models are complex by nature. Practitioners often consider model development an art and very different from other software-related disciplines. In this session we discuss how best practices from other areas of software development can still be applied to the implementation of mathematical models. Learn how to add structure to your model development process, work more efficiently and produce results faster. This session will also be an interesting introduction to optimization in practice.

For Fruits and Veggies with Gurobi and Azure. 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.

For an efficient electricity supply. In this talk, we will present OIIO, an innovative 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. We will focus on how to solve the problem of deciding the power to contract according to the billing precepts of the power term and the restrictions of the electricity marketers.

Gurobi in the Python Ecosystem

Finding an optimal solution can be quite challenging for some models. In this session, we discuss potential reasons for this and how to deal with it.
We also show a few techniques that help determine why some model is infeasible. Additionally, we give insights into the geometry of MIP to better understand the difficulty of models. Heuristics are essential to finding reasonable solutions quickly. We discuss heuristics included in the solver and other general concepts.

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.

This session will introduce you to the importance and fundamentals of Mathematical Optimization (MO).
You’ll learn how MO differs from other methods in AI, how it aids in making complex decisions by translating predictions into actionable solutions, and why it’s so valuable to add this expertise to your professional toolkit.
You’ll see how to identify the essential building blocks of optimization models in business problems: decision variables, constraints, and objective functions.
This session will also feature a hands-in example, guiding you through running your ‘Hello World!’ optimization model.

This session will introduce you to the importance and fundamentals of Mathematical Optimization (MO).
You’ll learn how MO differs from other methods in AI, how it aids in making complex decisions by translating predictions into actionable solutions, and why it’s so valuable to add this expertise to your professional toolkit.
You’ll see how to identify the essential building blocks of optimization models in business problems: decision variables, constraints, and objective functions.
This session will also feature a hands-in example, guiding you through running your ‘Hello World!’ optimization model.

Models with numerical issues can lead to undesirable results: slow performance, wrong answers or inconsistent behavior.
When solving a model with numerical issues, tiny changes in the model or machine can make a big difference in the results. In this session, you will learn about Gurobi’s guidelines on numerical issues, how to identify them, how they impact your solutions and, most importantly, how to avoid them.

Gurobi can handle a number of non-linear functions in optimization models. Historically, Gurobi first supported convex quadratic function in the objective and constraints. With Gurobi 9.0, Gurobi introduced a global solver for non-convex quadratic models and automatic piecewise-linear approximations of common arithmetic functions. Gurobi 11.0 comes with support for global optimization of models containing univariate non-linear functions . In this talk, we will review those various features and present our current work for tackling non-linearities better, including an outlook to Gurobi 12.