Name: Building Gurobi Models from Pandas Dataframes
Start: 2023-03-24T19:06:00Z
End: 2023-03-24T19:06:55.000Z
Location: BrightTALK
Rating: 5

With Gurobi’s decision intelligence technology, you can make optimal business decisions in seconds. From workforce scheduling, portfolio management, and marketing optimization, to supply chain design, and everything in between, Gurobi identifies your optimal solution, out of trillions of possibilities.  

As the leader in decision intelligence, Gurobi delivers easy-to-integrate, full-featured software and best-in-class support, with an industry-leading 98% customer satisfaction rating. 

This presentation demonstrates how Operational Research and Analytics methodologies can address critical challenges in modern healthcare, driving significant gains in both operational efficiency and service equity.

Academic Webinar: Life-Saving Optimization

We’re excited to invite you to our upcoming academic webinar featuring Dr. Can Li - Assistant Professor at Purdue University. This will be a broadcast of Dr. Li's presentation at this year's Gurobi Days Digital Academic.

Academic Webinar: Explaining Optimization Problems Using Large Language Models - Dr. Can Li (GDDA replay)

Are you making the right decisions for your supply chain network?
Watch this exclusive webinar with Wendy Wang, Director of Optimization Analytics Technology (OAT), as she shares use cases on how optimization can transform supply chain network planning and support better decision making. Wendy is part of the team that was a 2024 Franz Edelman Award finalist for groundbreaking work in supply chain optimization for a major fast-food chain.

What You’ll Learn:
How to improve daily transportation scheduling and stock replenishment to reduce costs and increase efficiency.
How to adjust network coverage and Stock Keeping Unit (SKU) mapping to make better seasonal and annual decisions.
How to determine the ideal number, size, and location of facilities to maximize performance and reduce costs.
How to optimize product and material flows to improve delivery speed and service levels.

Supply Chain Network Planning Optimization

Power systems all over the world are experiencing rapid and profound changes. The last decade has seen an inexorable rise in renewable generation technologies, such as wind and solar farms.

In the past, these technologies accounted for only a small fraction of total electricity supply. But now they are a critical part of our power system, and their significance will only continue to grow. These are profound changes that have consequences for every part of the energy sector.

Against this backdrop, there is now more focus than ever on how we can better understand the outlook for the power system. Despite the many detractors, wholesale market models are in widespread use across the energy sector. We seek to use these models to answer a wide variety of questions, including:

Finding the optimal combination of generation and transmission assets to invest in while maintaining low energy cost.
Defining the role of different technologies like wind, solar, storage, and gas in the future power system.
Retiring existing high-emissions generation without adversely impacting reliability, security and affordability of electricity.
Providing models to better understand, design, and conduct stress test configuration on the future power system digitally through the use of prevailing tools such as PLEXOS and Gurobi that are commonly used in the energy sector.
In this webinar, we will offer a high-level overview of how optimization can be used in the energy sector to better support decision-making.

Opening the Black Box: Understanding Optimization in the Energy Sector

In the age of sector coupling and volatile electricity markets, energy storage is key to unlocking resilient and profitable renewable energy systems.

Join us and our guest speaker Leon Haupt, Founder & Head of Business Development at Phelas GmbH—a pioneer in innovative energy storage solutions—to discover how Gurobi’s optimization engine drives smarter decision-making in energy infrastructure design, from techno-economic feasibility to operational strategy.

Learn how advanced mathematical models simplify the complexity of hybrid power plants and help future-proof your investments against regulatory and financial risks.

Smart Design of Energy Storage Projects - From Complexity to Clarity

This exclusive webinar hosted  Belma Şıpka from Hitit, a global Airline & Travel IT Solutions provider, explored how advanced optimization techniques can transform annual leave planning in the aviation industry.
Belma introduced a powerful leave planning model built with the Gurobi solver, designed to efficiently schedule airline crew leaves while honoring individual preferences and maintaining operational stability. The session highlighted how this solution enhances efficiency, reduces planning workload, and promotes fairness and transparency in workforce management.
From airline operations and crew planning to HR and data science, the webinar offered valuable insights for professionals seeking to streamline complex scheduling challenges with scalable, data-driven solutions.

Smooth Scheduling: Optimizing Annual Leave Planning in Aviation

In this webinar, Gurobi experts Silke Horn and Robert Luce dive into advanced portfolio optimization and backtesting with discrete constraints using Gurobi's MIP technology. Learn how to incorporate complex real-world factors like fixed costs, transaction limits, and cardinality constraints to optimize investment strategies. Watch to discover best practices, insights, and practical tips for unlocking alpha in your portfolio.

Unlocking Alpha with Gurobi

Quantum computers have received extensive publicity throughout the last 20 years, showing potential for major performance improvements over classical computers when it comes to challenging industrial problems. However, while progress is being made, questions remain about how to effectively translate the underlying theory into practical computers. The resulting debate between quantum enthusiasts and quantum skeptics can be difficult to navigate, especially for those who are new to the underlying concepts.

In Part I of this presentation, we explored the technical details of quantum computers, with a focus on quantum annealers. Now, in Part II, we discuss the logic gate architecture, revealing the underlying linear algebraic concepts that are familiar to many optimization practitioners.

The connections between classical mathematical optimization concepts and quantum computing can get lost in the collection of different concepts and notations associated with the latter. However, a closer look reveals that numerous mathematical optimization concepts can help us learn how to solve challenging problems in a quantum computing environment.

We examine how the entanglement of qubits results in significant computational power, and how that power can be harnessed into algorithms that can potentially solve complex problems much faster compared to a classical computing environment.

An Intro to Quantum Computing for Optimization Practitioners Part II: Logic Gate Quantum Computers

Quantum computers have received extensive publicity over the last 20 years, promising potentially major performance gains over classical computers for a range of industrial problems. However, while progress is being made, questions remain about how to translate the underlying theory into practical computers. The resulting debate between quantum enthusiasts and quantum skeptics can be difficult to navigate, especially for those who have little or no familiarity with the underlying concepts.

This tech talk will not take sides in the debate, nor make predictions about quantum computing capabilities of the future. Rather, it will try to present the technical details of quantum computers in a way that resonates with mathematical optimization practitioners. It’s easy to overlook the connections between classical mathematical optimization concepts and quantum computing. However, many mathematical optimization concepts can offer guidance for solving challenging problems in a quantum computing environment.

An Introduction to Quantum Computing for Optimization Practitioners, Part I: Quantum Annealers

Data science, machine learning, artificial intelligence, and quantum computing have been the subject of much publicity in recent years. While quantum computing is still more in a developmental phase, the other three are already used extensively in industry and other operations. Meanwhile, mathematical programming, a technology that started more than 70 years ago on early computers, is quietly at work all around us, touching our lives every day. 

Because mathematical programming remains behind the scenes and less publicly visible, a false impression can emerge that it is a separate technology that competes with these other, more well-known technologies, or that it has even been made obsolete by them. This is indeed a misconception, as evidenced by the steady increase in its use over the last few decades across a growing number of industries.  

In this webinar hosted by Gurobi’s Dr. Ed Klotz, Senior Mathematical Optimization Specialist, and Jerry Yurchisin, Senior Data Science Strategist, they explore: 

-The complementary relationship between data science, AI, machine learning, and mathematical programming 
-How data science and machine learning can enhance mathematical programming models 
-How mathematical programming skills can position you for the quantum future.

Mathematical Programming and the Buzzword Jungle

Our society is critically dependent on reliable algorithms, especially optimisation algorithms to support complex decision making, but establishing trust is a growing concern. How do we know that our algorithmic choices – whether it be a heuristic or exact solver, or a model, or parameter configurations  – are reliable in practice? This vexing issue relies on testing algorithms with enough unbiased test instances to gain insights into strengths and weaknesses under various conditions.


This talk will provide a brief introduction to Instance Space Analysis as a methodology that provides a mathematically rigorous foundation for “stress-testing” algorithms. ISA exposes the strengths and weaknesses of algorithms and supports the generation of rich and diverse test problems to understand algorithm reliability under a variety of conditions.


The methodology has been made available via an online tool (matilda.unimelb.edu.au) that has been adopted worldwide by researchers in many fields, and is supporting industry partners keen to avoid disasters when deploying critical algorithms. The webinar will use several case studies from timetabling and bin packing to highlight how insights into performance of heuristics, MIP models or solvers can be obtained by generating and testing diverse instances with ISA.

Stress-testing Algorithms, Solvers, and Models via Instance Space Analysis

New Educational Case Study: Optimizing Humanitarian Aid with STOCKHOLM

In collaboration with ESUPS (Emergency Supply Prepositioning Strategy), Gurobi presents a powerful new educational case study featuring the STOCKHOLM platform—a visual mapping and analysis tool used to coordinate relief stock across humanitarian organizations.

Named for STOCK of Humanitarian Organizations Logistics Mapping, the platform symbolizes connection and collaboration, helping break down silos in disaster response. Students and instructors can now explore the full journey from business problem to real-world optimization model—supporting faster, smarter decisions in humanitarian logistics.

This case gives learners hands-on experience with the math, data, and code that powers global disaster relief—and shows how optimization can drive real-world impact.

Gurobi Academic Webinar with ESUPS: Full Educational Case Study Review

There is a common consensus that state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms are powerful in terms of their accuracy, but they are also perceived as opaque not being transparent about how they arrive at their decisions.  This prevents the adoption of these powerful algorithms in Data-Driven Decision-Making. Even when in place, they can have a detrimental impact on the citizen, and there are well-documented examples of discriminatory outcomes in high-stakes algorithmic decision-making. Therefore, there is an urgent need to strike a balance between three goals, namely, accuracy, explainability and fairness.

In this talk, we will navigate through some of the latest advances in Mathematical Modelling and Optimization to enhance the transparency and fairness of ML algorithms. We will first focus on the training of ML models that trade off accuracy, explainability and fairness. Then, we will focus on the task of providing explanations to an existing ML model by means of the burgeoning field of Counterfactual Analysis.

On a (Short) Optimization Tour Through Transparent and Fair ML with Prof. Dolores Morales

Join us for our Gurobi Days Digital Academic webinar series, focusing on Optimization for Good and featuring top academics changing the world with mathematical optimization. Dr. Özlem Ergun joins us to share some of the impactful research and collaborations she has led with global non-profits harnessing the power of optimization.

ABSTRACT: In this talk, Dr. Ergun will describe her efforts in optimizing global food aid operations using mathematical programming in collaboration with UN World Food Program and USAID for over 15 years.

Gurobi Academic Webinar with Dr. Ozlem Ergun: Optimizing Global Food Aid Operations

In the consumer goods and manufacturing industries, maintaining peak performance is essential. Companies strive for excellence, aiming to maximize production throughput and uphold the highest quality of standards, while minimizing both operating and raw material costs. However, traditional supply chain planning often centers solely on cost, which can compromise production efficiency and product quality—ultimately impacting customer satisfaction and fulfillment metrics.
 
In order to ensure optimal raw material procurement planning, a more comprehensive approach is needed—one that balances cost with key performance indicators like overall equipment effectiveness, production yield, and the value in use for raw materials.
 
Join us for an exclusive webinar led by Rajib Saha, co-founder and CEO at Parabole.ai, and Aditya Narayanamoorthy, Architect at Parabole.ai, as they discuss how Parabole’s procurement plan optimization agent—powered by Parabole’s causal AI and the Gurobi solver—addresses this gap by synthesizing causality across production efficiency and economic factors.

Multi-objective Procurement Planning Optimization Agents Built on Causal AI and the Gurobi Solver

Join us on Aug. 28th at 11AM EDT! 

Optimization Over Trained Neural Networks: Taking a Relaxing Walk
 
Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compression, and optimization with learned constraints. However, solving these formulations soon becomes difficult as the network size grows due to the weak linear relaxation and dense constraint matrix. We have seen improvements in recent years with cutting plane algorithms, reformulations, and a heuristic based on Mixed-Integer Linear Programming (MILP). In this work, we propose a more scalable heuristic based on exploring global and local linear relaxations of the neural network model.

Gurobi Academic Webinar with Dr. Thiago Serra - Optimization Over Trained Neural Networks: Taking a Relaxing Walk

Learn tips on how to choose a mathematical programming solver to help you tackle your complex business problems.

Choosing a Math Programming Solver

As the de-facto standard in Python for data manipulation and analysis, pandas is deeply integrated into many analytics projects and tools. Gurobi’s Python API, gurobipy, does not directly interact with standard pandas data structures, so incorporating decision optimization into data science applications is not as straightforward as it could be. For better interoperability between the two libraries, what is needed is a higher-level syntax to build optimization models directly from suitably structured pandas data.

Our solution is gurobipy-pandas, a convenient wrapper library to connect pandas with gurobipy. It enables users to efficiently build mathematical optimization models from data stored in DataFrames and Series, and to extract solutions as pandas objects. This webinar will walk through basic concepts and complete modeling examples to demonstrate best practices for using this library.

Building Gurobi Models from Pandas Dataframes

pytorch

keras

linear programming

scikit-learn

neural network

Python

Machine Learning

mathematical optimization

decision analytics

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Building Gurobi Models from Pandas Dataframes

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