Supply Chain
Decisiveness During Uncertainty
Optimize your supply chain planning, decision making, and operations to keep supply and demand in balance.
Supply Chain
Decisiveness During Uncertainty
Optimize your supply chain planning, decision making, and operations to keep supply and demand in balance.
Supply Chain
Decisiveness During Uncertainty
Optimize your supply chain planning, decision making, and operations to keep supply and demand in balance.
Overview
Leading companies across numerous industries use Gurobi’s mathematical optimization solver – in a wide variety of applications – to optimize their supply chain planning, decision making, and operations, and to keep supply and demand in balance.
With mathematical optimization, you can:
Attain visibility and control over your end-to-end supply chain network.
React and respond rapidly and effectively to changing conditions and disruptions across your supply chain.
Make dynamic, data-driven decisions that optimize your company’s efficiency and profitability.
Achieve your business goals by balancing cost and service-level tradeoffs – simultaneously satisfying customer demand and spurring bottom-line growth.
Transform your supply chain from a source of costs into a source of competitive advantage.
Overview
Leading companies across numerous industries use Gurobi’s mathematical optimization solver – in a wide variety of applications – to optimize their supply chain planning, decision making, and operations, and to keep supply and demand in balance.
With mathematical optimization, you can:
Attain visibility and control over your end-to-end supply chain network.
React and respond rapidly and effectively to changing conditions and disruptions across your supply chain.
Make dynamic, data-driven decisions that optimize your company’s efficiency and profitability.
Achieve your business goals by balancing cost and service-level tradeoffs – simultaneously satisfying customer demand and spurring bottom-line growth.
Transform your supply chain from a source of costs into a source of competitive advantage.
Overview
Leading companies across numerous industries use Gurobi’s mathematical optimization solver – in a wide variety of applications – to optimize their supply chain planning, decision making, and operations, and to keep supply and demand in balance.
With mathematical optimization, you can:
Attain visibility and control over your end-to-end supply chain network.
React and respond rapidly and effectively to changing conditions and disruptions across your supply chain.
Make dynamic, data-driven decisions that optimize your company’s efficiency and profitability.
Achieve your business goals by balancing cost and service-level tradeoffs – simultaneously satisfying customer demand and spurring bottom-line growth.
Transform your supply chain from a source of costs into a source of competitive advantage.
Peak Under the Hood
Dive deep into sample models, built with our Python API.
Market Sharing
In this example, we’ll show you how to solve a goal programming problem that involves allocating the retailers to two divisions of a company in order to optimize the trade-offs of several market sharing goals. You’ll learn how to create a mixed integer linear programming model of the problem using the Gurobi Python API and how to find an optimal solution to the problem using the Gurobi Optimizer.
This model is example 13 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 267-268 and 322-324. 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. You may also want to check out the documentation of the Gurobi Python API.
Supply Network Design
Traveling Salesman
Peak Under the Hood
Dive deep into sample models, built with our Python API.
Market Sharing
In this example, we’ll show you how to solve a goal programming problem that involves allocating the retailers to two divisions of a company in order to optimize the trade-offs of several market sharing goals. You’ll learn how to create a mixed integer linear programming model of the problem using the Gurobi Python API and how to find an optimal solution to the problem using the Gurobi Optimizer.
This model is example 13 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 267-268 and 322-324. 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. You may also want to check out the documentation of the Gurobi Python API.
Supply Network Design
Traveling Salesman
"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
SAP: Mastering Supply Chain Challenges Through Complex Scenario Planning
SAP integrates Gurobi across its cloud portfolio, delivering cutting-edge supply chain scenario planning to enterprise customers.
Case Studies
Delhivery: Making the Last Mile More Efficient
Delhivery optimizes last-mile delivery routes across India, fulfilling over 1 billion orders with 24/7 efficiency.
Case Studies
LeanLogistics: Supply Chain Optimization
LeanLogistics optimizes freight planning for millions of orders daily, unlocking significant cost savings across global supply chains.
Additional Insights
Case Studies
Case Studies
SAP: Mastering Supply Chain Challenges Through Complex Scenario Planning
SAP integrates Gurobi across its cloud portfolio, delivering cutting-edge supply chain scenario planning to enterprise customers.
Case Studies
Delhivery: Making the Last Mile More Efficient
Delhivery optimizes last-mile delivery routes across India, fulfilling over 1 billion orders with 24/7 efficiency.
Case Studies
LeanLogistics: Supply Chain Optimization
LeanLogistics optimizes freight planning for millions of orders daily, unlocking significant cost savings across global supply chains.
Additional Insights
Case Studies
Case Studies
SAP: Mastering Supply Chain Challenges Through Complex Scenario Planning
SAP integrates Gurobi across its cloud portfolio, delivering cutting-edge supply chain scenario planning to enterprise customers.
Case Studies
Delhivery: Making the Last Mile More Efficient
Delhivery optimizes last-mile delivery routes across India, fulfilling over 1 billion orders with 24/7 efficiency.
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.
