Sports

Make an Optimal Impact

Identify the most exciting—and most fair—matchup schedule possible, while taking all of your constraints into account.

Sports

Make an Optimal Impact

Identify the most exciting—and most fair—matchup schedule possible, while taking all of your constraints into account.

Sports

Make an Optimal Impact

Identify the most exciting—and most fair—matchup schedule possible, while taking all of your constraints into account.

Overview


Optimization in sports scheduling can be considered as much art as science in combining a fascinatingly varied amount of data into a schedule that impacts players and audiences from around the world each season. Gurobi provides planners with the decision intelligence insights they need to evaluate any given possible schedule, identify what they want to change, and see the resulting effects until the optimal end result is achieved.

Overview


Optimization in sports scheduling can be considered as much art as science in combining a fascinatingly varied amount of data into a schedule that impacts players and audiences from around the world each season. Gurobi provides planners with the decision intelligence insights they need to evaluate any given possible schedule, identify what they want to change, and see the resulting effects until the optimal end result is achieved.

Overview


Optimization in sports scheduling can be considered as much art as science in combining a fascinatingly varied amount of data into a schedule that impacts players and audiences from around the world each season. Gurobi provides planners with the decision intelligence insights they need to evaluate any given possible schedule, identify what they want to change, and see the resulting effects until the optimal end result is achieved.

Explore real-world problems in your industry

Dive deep into sample models, built with our Python API.

Manpower Planning

Staffing problems – which require difficult decisions about the recruitment, training, layoffs, and scheduling of workers – are common across a broad range of manufacturing and service industries. In this example, you’ll learn how to model and solve a complex staffing problem by creating an optimal multi-period operation plan that minimizes the total number of layoffs and costs. More information on this type of model can be found in example #5 of the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 256 – 257 and 303 – 304. This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.

 Learn More


Marketing Campaign Optimization

Workforce Scheduling

Explore real-world problems in your industry

Dive deep into sample models, built with our Python API.

Manpower Planning

Staffing problems – which require difficult decisions about the recruitment, training, layoffs, and scheduling of workers – are common across a broad range of manufacturing and service industries. In this example, you’ll learn how to model and solve a complex staffing problem by creating an optimal multi-period operation plan that minimizes the total number of layoffs and costs. More information on this type of model can be found in example #5 of the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 256 – 257 and 303 – 304. This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.

 Learn More


Marketing Campaign Optimization

Workforce Scheduling

Explore real-world problems in your industry

Dive deep into sample models, built with our Python API.

Manpower Planning

Staffing problems – which require difficult decisions about the recruitment, training, layoffs, and scheduling of workers – are common across a broad range of manufacturing and service industries. In this example, you’ll learn how to model and solve a complex staffing problem by creating an optimal multi-period operation plan that minimizes the total number of layoffs and costs. More information on this type of model can be found in example #5 of the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 256 – 257 and 303 – 304. This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.

 Learn More


Marketing Campaign Optimization

Workforce Scheduling

"Gurobi's advancements go beyond performance to enhance usability, appealing to both beginner and expert users."

Gurobi 13.0 Beta Tester

"We’ve been doing optimization for decades, and Gurobi is simply the fastest and most reliable solver we’ve tested. We use it on every project."

"It’s not just about getting the best answer; it’s about giving advisors and clients a plan they can understand and trust. What would take hours to do manually, we can do in minutes, with better outcomes."

"Gurobi's advancements go beyond performance to enhance usability, appealing to both beginner and expert users."

Gurobi 13.0 Beta Tester

"We’ve been doing optimization for decades, and Gurobi is simply the fastest and most reliable solver we’ve tested. We use it on every project."

"It’s not just about getting the best answer; it’s about giving advisors and clients a plan they can understand and trust. What would take hours to do manually, we can do in minutes, with better outcomes."

"Gurobi's advancements go beyond performance to enhance usability, appealing to both beginner and expert users."

Gurobi 13.0 Beta Tester

"We’ve been doing optimization for decades, and Gurobi is simply the fastest and most reliable solver we’ve tested. We use it on every project."

"It’s not just about getting the best answer; it’s about giving advisors and clients a plan they can understand and trust. What would take hours to do manually, we can do in minutes, with better outcomes."

gurobi optimizer

The Solver that Does More

Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.

Unmatched Performance
Continuous Innovation
Responsive, Expert Support
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Unmatched Performance

With Gurobi’s advanced algorithms, you can add complexity to your models to better represent real-world systems—and still solve them within the available time.

Significant speed-ups across all major problem types, achieving a 92x improvement in MILP performance since version 1.1
Tuned to optimize performance over a wide range of instances and applications
Rigorously tested for numerical stability and correctness using an internal library of more than 10,000 industry and academic models
Learn More

Frequently Asked Questions

What is prescriptive analytics?

Prescriptive analytics tools like mathematical optimization help you make decisions based on your real-world business goals (“objectives”) and limitations (“constraints.”) This can be especially useful when you’re facing a business problem with multiple, conflicting goals (such as cutting spending while increasing production) and multiple constraints (such as time, distance, product availability).

Learn more about prescriptive analytics in our article, “What is Prescriptive Analytics?”

What is the difference between predictive and prescriptive analytics?

What are some examples of prescriptive analytics in the real world?

How can prescriptive and predictive analytics work together?

What is the primary goal of prescriptive analytics?

What are the techniques used in prescriptive analytics?

What is prescriptive analytics also known as?

Frequently Asked Questions

What is prescriptive analytics?

Prescriptive analytics tools like mathematical optimization help you make decisions based on your real-world business goals (“objectives”) and limitations (“constraints.”) This can be especially useful when you’re facing a business problem with multiple, conflicting goals (such as cutting spending while increasing production) and multiple constraints (such as time, distance, product availability).

Learn more about prescriptive analytics in our article, “What is Prescriptive Analytics?”

What is the difference between predictive and prescriptive analytics?

What are some examples of prescriptive analytics in the real world?

How can prescriptive and predictive analytics work together?

What is the primary goal of prescriptive analytics?

What are the techniques used in prescriptive analytics?

What is prescriptive analytics also known as?

Additional Insights

Case Studies

Case Studies

Light Up the World: Bringing Clean Energy and Internet Access to Remote Communities

Light Up the World uses optimization to design off-grid solar systems for remote Peruvian communities with no room for error.

Case Studies

Emesa: Marketing Campaign Optimization

Emesa drives higher traffic and revenue by delivering personalized email campaigns to the right customers at the right time.

Case Studies

National Football League Scheduling

The NFL uses optimization to schedule 256 games across 17 weeks, balancing trillions of possibilities into a perfect season plan.

Additional Insights

Case Studies

Case Studies

Light Up the World: Bringing Clean Energy and Internet Access to Remote Communities

Light Up the World uses optimization to design off-grid solar systems for remote Peruvian communities with no room for error.

Case Studies

Emesa: Marketing Campaign Optimization

Emesa drives higher traffic and revenue by delivering personalized email campaigns to the right customers at the right time.

Case Studies

National Football League Scheduling

The NFL uses optimization to schedule 256 games across 17 weeks, balancing trillions of possibilities into a perfect season plan.

Additional Insights

Case Studies

Case Studies

Light Up the World: Bringing Clean Energy and Internet Access to Remote Communities

Light Up the World uses optimization to design off-grid solar systems for remote Peruvian communities with no room for error.

Case Studies

Emesa: Marketing Campaign Optimization

Emesa drives higher traffic and revenue by delivering personalized email campaigns to the right customers at the right time.

Start Solving with Gurobi

Try Gurobi on your own optimization models and see how it performs on real decision problems.

Start Solving with Gurobi

Try Gurobi on your own optimization models and see how it performs on real decision problems.

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