Optimization for the Industrial Automation and Machinery Industry - Gurobi Optimization (original) (raw)
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.
The Solver That Does More
Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.
Responsive, Expert Support
Responsive, Expert Support
Gurobi Optimizer Delivers Unmatched Performance
Unmatched Performance
With our powerful algorithms, you can add complexity to your model to better represent the real world, and still solve your model within the available time.
- The performance gap grows as model size and difficulty increase.
- Gurobi has a history of making continual improvements across a range of problem types, with a more than 75x speedup on MILP since version 1.1.
- Gurobi is tuned to optimize performance over a wide range of instances.
- Gurobi is tested thoroughly for numerical stability and correctness using an internal library of over 10,000 models from industry and academia.
- Gurobi Optimizer Delivers Continuous Innovation
Continuous Innovation
Our development team includes the brightest minds in decision-intelligence technology--and they're continually raising the bar in terms of solver speed and functionality.
- Our code is fundamentally parallel—not sequential code that was parallelized—so you can make the most of parallelism and run sequentially.
- We go beyond cutting-edge MIP cutting planes, with new classes of cuts you can find only with Gurobi.
- Our advanced MIP heuristics identify feasible, good quality solutions, fast—where other solvers fall flat.
- Our barrier algorithms fully exploit the features of the latest computer architectures.
- Our APIs are lightweight, modern, and intuitive—to minimize your learning curve while maximizing your productivity.
- Gurobi Optimizer Delivers Responsive, Expert Support
Responsive, Expert Support
Our PhD-level experts are here when you need them—ready to provide comprehensive guidance and technical support. They bring deep expertise in working with commercial models and are there to assist you throughout the process of implementing and using Gurobi.
- Tap into our team’s deep expertise—from implementation to tuning and more.
- We respond to customer inquiries in hours not days, helping to quickly resolve any issues you’re facing.
- We can help you fit and adapt your mathematical optimization application to your changing requirements.
Peek Under the Hood
Dive deep into sample models, built with our Python API.
Technician Routing & Scheduling
Technician Routing & Scheduling
Manpower Planning
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
- Supply Network Design
Supply Network Design
Supply Network Design I
Try this Jupyter Notebook Modeling Example to learn how to solve a classic supply network design problem that involves finding the minimum cost flow through a network. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine the best way to satisfy customer demand while minimizing shipping costs. This model is example 19 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 273-275 and 330-332. This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.
Supply Network Design II
Take your supply chain network design skills to the next level in this example. We’ll show you how – given a set of factories, depots, and customers – you can use mathematical optimization to determine which depots to open or close in order to minimize overall costs. This model is example 20 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 275-276 and 332-333 This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.
Learn More
- Technician Routing and Scheduling Problem
Technician Routing & Scheduling
Try this modeling example to discover how mathematical optimization can help telecommunications firms automate and improve their technician assignment, scheduling, and routing decisions in order to ensure the highest levels of customer satisfaction. This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you have some knowledge about building mathematical optimization models. To fully understand the content of this notebook, you should be familiar with object-oriented-programming.
Learn More
- Workforce Scheduling
Workforce Scheduling
In this example, you’ll learn how to solve a critical, central problem in the services industry: workforce scheduling. We’ll demonstrate how you can use mathematical optimization to generate an optimal workforce schedule that meets your business requirements, maximizes employee fairness and satisfaction, and minimizes the number of temporary workers your company needs to hire. 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
Frequently Asked Questions
What is mathematical optimization?
Mathematical optimization uses the power of math to find the best possible solution to a complex, real-life problem. You input the details of your problem—the goals you want to achieve, the limitations you’re facing, and the variables you control—and the mathematical optimization solver will calculate your optimal set of decisions.
What’s a real-world example of mathematical optimization?
80% of the world’s leading companies use mathematical optimization to make optimal business decisions. For example, Air France uses it to build the most efficient schedule for its entire fleet, in order to save on fuel and operational costs, while reducing delay propagation.
What makes mathematical optimization “unbiased”?
Descriptive and predictive analytics show you what has happened in the past, why it happened, and what’s likely to happen next. But to decide what to do with that information, you need human input—which can introduce bias.
With mathematical optimization, you receive a decision recommendation based on your goals, constraints, and variables alone. You can, of course, involve human input when it comes to whether or not to act on that recommendation. Or you can bypass human input altogether and automate your decision-making.
Additional Insight
How to Optimize the Design of Your Supply Chain Network
Mathematical Optimization: An Established and Ever-Evolving Tool For Tackling Supply Chain Disruption
Supply Chain Webinar: Maximize Your Resources and Operational Efficiency
How Supply Chain Companies Can Achieve Decision-Centric Optimization
Guidance for Your Journey
30 Day Free Trial for Commercial Users
Start solving your most complex challenges, with the world's fastest, most feature-rich solver.
Always Free for Academics
We make it easy for students, faculty, and researchers to work with mathematical optimization.