Industrial Automation & Machinery
Make Better Strategic Decisions
Optimize your scheduling and production processes, increase efficiency, and reduce costs.
Industrial Automation & Machinery
Make Better Strategic Decisions
Optimize your scheduling and production processes, increase efficiency, and reduce costs.
Industrial Automation & Machinery
Make Better Strategic Decisions
Optimize your scheduling and production processes, increase efficiency, and reduce costs.
Overview
With Gurobi, manufacturers can optimize their scheduling and production processes—to increase efficiency and reduce costs. It also helps business managers combine improvements in manufacturing processes with the related supply chain and distribution systems.
Overview
With Gurobi, manufacturers can optimize their scheduling and production processes—to increase efficiency and reduce costs. It also helps business managers combine improvements in manufacturing processes with the related supply chain and distribution systems.
Overview
With Gurobi, manufacturers can optimize their scheduling and production processes—to increase efficiency and reduce costs. It also helps business managers combine improvements in manufacturing processes with the related supply chain and distribution systems.
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.
Supply Network Design
Technician Routing & Scheduling
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.
Supply Network Design
Technician Routing & Scheduling
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.
Supply Network Design
Technician Routing & Scheduling
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."
The Solver that Does More
Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.
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.
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
Metallurgical Additives Producer: Achieving Faster Resource Optimization and an Estimated $1.5M in Annual Savings
A metallurgical additives producer saves an estimated $1.5M annually by optimizing resource allocation with Aimpoint Digital.
Case Studies
Arauco: Supply Chain Planning Optimization
Arauco balances supply and demand across its global wood production network, cutting costs and boosting customer satisfaction.
Case Studies
Complevo: Optimal Workforce Planning
Complevo's FREI ZEIT tool eliminates scheduling chaos at bakeries, boosting staff satisfaction and planning transparency.
Additional Insights
Case Studies
Case Studies
Metallurgical Additives Producer: Achieving Faster Resource Optimization and an Estimated $1.5M in Annual Savings
A metallurgical additives producer saves an estimated $1.5M annually by optimizing resource allocation with Aimpoint Digital.
Case Studies
Arauco: Supply Chain Planning Optimization
Arauco balances supply and demand across its global wood production network, cutting costs and boosting customer satisfaction.
Additional Insights
Case Studies
Case Studies
Metallurgical Additives Producer: Achieving Faster Resource Optimization and an Estimated $1.5M in Annual Savings
A metallurgical additives producer saves an estimated $1.5M annually by optimizing resource allocation with Aimpoint Digital.
Case Studies
Arauco: Supply Chain Planning Optimization
Arauco balances supply and demand across its global wood production network, cutting costs and boosting customer satisfaction.
Case Studies
Complevo: Optimal Workforce Planning
Complevo's FREI ZEIT tool eliminates scheduling chaos at bakeries, boosting staff satisfaction and planning transparency.
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.
