Gurobi Optimization Tutorial

[Coleman John]

Jointhe conference exhibitors as they discuss innovations and best practices in the field. Professional Development Units (PDUs) are available to those who attend these sessions. All attendees are welcome to join during the scheduled time.

Discover the game-changing potential of AI-powered models in optimizing the decision-making process for deceased donor kidney transplantation. Unleash the power of advanced deep learning techniques to revolutionize patient care with streamlined processes. Experience the real-time identification of key features that can confidently leverage decision-making and significantly reduce the non-utilization of deceased donor kidneys. Join us in embracing the future of healthcare with AI-powered models.

Supply chain leaders have continuously tried to expand and enhance capabilities by using advanced techniques and analytics. JD.com, the largest retailer in China based on revenue, is committed to an intelligent, integrated, and resilient supply chain that creates value for all players within the retail ecosystem. Despite challenges in the complex and sophisticated retail supply chain, JD.com has strengthened its supply chain agility, and attained shared value by focusing on supply chain efficiency, supply chain resilience, and reducing supply chain uncertainty and volatility. This presentation will help you uncover more supply chain capabilities using advanced techniques with real-world cases adopted by JD.com.

Optimization applications combine technology and expertise from many different areas, including model-building, algorithms, and data-handling. Often, the gathering, pre/post-processing, and visualization of the data is done by a diverse organization-spanning group that shares a common bond: their skill in and appreciation for Python and the vast array of available packages it provides. For this reason, GAMS offers multiple ways to integrate with Python on the data-handling side, as well as offering some packages of our own (e.g. GAMS Transfer, GAMS Connect). In this talk, we will explore the benefits of this integration and demonstrate them using a real-world example complete with results on performance.

Discover how Topological Data Analysis (TDA) can be used in complex data analysis projects such as cyber security, aircraft engine analysis, fraud detection, and autonomous vehicles. Observe data connections via causal discovery and uncover distinct clusters exhibiting unforeseen patterns through the utilization of the DataRefiner platform. This talk will equip attendees with practical knowledge of how TDA can help discover novel patterns in their areas of interest.

The renewed enthusiasm for artificial intelligence (A.I.) and, more particularly, for techniques based on deep learning and other forms of neural networks, means that we are trying to apply these latest techniques to all problems requiring a supervised or unsupervised form of learning. But this unprecedented wave of interest often makes us forget there are other forms of machine learning that have proven themselves over time. During this presentation we will compare certain forms of machine learning with and without the contribution of neural network techniques in order to assess the importance and the nature of a possible contribution (if any). To do this, we will examine different tasks in the field of automatic language processing, namely topic modeling, automatic word disambiguation, and the development of semantic lexicons. We will also try to identify in which context an approach based on neural networks or deep learning deserves consideration.

In this tutorial, attendees will get a first look at the additional support for nonlinear functions available in Gurobi 11.0. Previous versions of Gurobi supported a set of frequently used general nonlinear functions through piecewise linear approximation. Gurobi 11.0 extends the spatial branch and bound algorithm that supported nonconvex quadratic constraints and objectives starting with version 9.0 to handle more general nonlinear constraints and objectives, including higher degree polynomial, logarithmic, exponential and trigonometric functions. This tutorial will discuss how to extend the McCormick relaxation used in the spatial branch and bound to these more general nonlinear functions, and the resulting implications regarding how to get good performance.

Artelys Knitro is a leading solver focused on large-scale, nonlinear (potentially non-convex), optimization problems. Knitro offers both interior-point and active-set algorithms for continuous models, as well as tools for handling problems with integer variables and other discrete structures. This tutorial will introduce the key features of Knitro and demonstrate how to use Knitro to model and solve an optimization problem from within the python environment by working through a real-world application in the energy industry. We will also highlight some of the latest developments in Knitro, focusing on some of the recent advances in solving mixed-integer nonlinear problems, and heuristics for finding global (or improved local) solutions for non-convex problems.

With 15,300 MW of installed capacity in 8 countries and 85% of hydropower capacity, Brookfield Renewable is one of the biggest hydropower producers worldwide. Artelys has worked closely with Brookfield Renewable to model and optimize the operations of 2 of their major hydropower plants (650 MW of installed capacity in Pennsylvania, USA). The objective was to develop a software solution to model hydropower plant operations. Artelys carried out a study that led to around 10% potential gain in the annual generated revenue and implemented a software solution based on Artelys Crystal Energy Planner to optimize short-term schedules for the 2 hydropower plants. Using Artelys Crystal Energy Planner, Artelys modelled the Brookfield Renewable system considering all specific operational and market-related constraints to take advantage of all the sources of flexibility to automatically generate reliable least cost production schedules.

This customized solution uses a powerful optimization engine to assist hydropower producers in maximizing their generation benefit while taking into account all specific operational, environmental, and market-related constraints. The objective of the presentation is to present the solution, with a focus on the steps taken to integrate these powerful algorithms into an operational scheduling process.

After 24 years there is a new version of Littlefield! Littlefield is a competitive online simulation of either a factory or a medical laboratory that has been by more than half a million students in 500+ universities in 60+ countries to excite and engage students in operations management topics like process analysis and inventory control. This presentation will introduce a newly updated version 2 of the game that will go into production in 2024.

? AI as your Research Partner: Imagine having an AI collaborator that helps you navigate the sea of information effortlessly. Learn how AI can assist researchers in sifting through vast datasets, identifying trends, and generating valuable hypotheses, propelling your research to new heights.

? Global Collaboration: Uncover the potential of AI in bridging geographical gaps and fostering cross-border collaboration. Explore our AI-powered translation tool that enable seamless communication and idea exchange among researchers and authors from around the world.

A feature overview of the browser-based CoCalc software platform, including collaboratively creating programming scripts and scientific publications, all while leveraging the power of tools like GitHub and Open AI with an integrated stack of your favorite open-source applications/languages and flexible cloud compute resources.

With more and more electric vehicles connecting to the power grid every day, there are concerns that existing grid infrastructure will be strained beyond acceptable operational limits. We can address these concerns by bringing operations, pricing, and forecasting into techno-economic models of power systems in MATLAB. Using these models, we can assess feasibility, risk, optimal operations, and profitability of charging infrastructure.

In this tutorial, we consider a scenario where a system operator can command individual electric vehicle battery units to both store and supply electricity while connected to the grid. The operator applies techno-economic optimization in MATLAB to the charging profiles to minimize electricity cost while accounting for system requirements and constraints, such as limits on state of charge, grid supply, and charge/discharge rate. The optimization provides a fast and automated approach for leveraging all of the units connected to the grid for overall system benefit. Charging profiles are then assessed for the impact on voltage and power flow levels using a grid-level simulation.

OPTMODEL from SAS provides a powerful and intuitive algebraic optimization modeling language and unified support for building and solving LP, MILP, QP, conic, NLP, constraint programming, network-oriented, and black-box models. This tutorial will include an overview of the optimization capabilities and demonstrate recently added features.

AIZOTH provides AI services such as Multi-Sigma, AI consulting, spot support to optimize manufacturing conditions, and commissioned R&D. Multi-Sigma is the cloud-based AI software for R&D to reduce the effort of experiment drastically and also to help researchers finding the innovative solutions for their actual problems with minimum experimental dataset. Multi-Sigma was already introduced by large manufacturing enterprises and top universities. We will demonstrate how a Multi-Sigma can be used with sample datasets.

Gurobi is a powerful optimization software and an alternative to Cplex for solving. Gurobi has some additionnal features compared to Cplex. For example, it can perform Mixed-Integer Quadratic Programming (MIQP) and Mixed-Integer Quadratic Constrained Programming (MIQCP).

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