To get the most out of a battery energy storage system (BESS), an optimal operational schedule is required. Gurobi can efficiently compute this optimal schedule.

This model is an example of a railway dispatching problem formulated as an optimization problem using the Gurobi Python API and solved with the Gurobi Optimizer.

This documentation provides several self-contained Jupyter notebooks that discuss the modeling of typical features in mean-variance (M-V) portfolio optimization.

Create a predictive model to forecast sales based on product prices, then build an optimization model to identify the optimal product mix.

With text-generation AI becoming popular and ubiquitous, can mathematical optimization catch cases of plagiarism?

Weather events are a major threat to the airline industry. The unpredictable nature of snowstorms, heavy rains, and icy runways make it difficult for aviation planners to make accurate schedules.

The electricity grid powers nearly every aspect of modern life — be it charging a phone, powering a factory, or mining Bitcoin. Managing the supply and demand of electricity can be a complex and challenging task. In this notebook, we model this decision-making problem using mathematical optimization.

Transporting food in a global transportation network is a challenging undertaking. In this notebook, we will build an optimization model to set up a food supply chain based on real data from the UN World Food Program.

They say when life gives you avocados, make guacamole. Just like the perfect guacamole needs the right blend of onion, lemon and spices, finding an optimal avocado price needs the right blend of descriptive, predictive and prescriptive analytics.

A good song never gets old, except when it does. In this notebook, we will create a music recommendation system using a mixture of predictive and prescriptive analytics.

A simple covering problem that builds a network of cell towers to provide signal coverage to the largest number of people possible.

This cutting stock problem with multiple master rolls is an example of combinatorial optimization problems that cannot be attacked with machine learning techniques due to the astronomical number of possibilities in the solution space. Also, open-source optimization solvers do not scale to real size cutting stock problems.

This demo integrates machine learning and mixed integer programming technologies to address the resource matching optimization problem - providing workforce resources with the right skills and capabilities, for the right job, at the right time, location, and cost.

Arrange X’s and O’s on a three-dimensional Tic-Tac-Toe board to minimize the number of completed lines or diagonals.

A problem of fitting a linear function to a set of observations is formulated as a linear programming problem.

A facility location problem that involves building warehouses to supply a certain number of supermarkets.

A marketing campaign optimization problem common in the banking and financial services industry.

Allocate retailers to two divisions of a company in order to optimize the trade-offs of several market sharing goals.

Minimize the cost of laying underwater cables that collect electricity produced by an offshore wind farm.

Determine how to satisfy customer demand, while minimizing shipping costs.

This COVID-19 Healthcare Facility Capacity Optimization problem shows you how to determine the optimal location and capacity of temporary healthcare facilities.

Determine the prices and demand for the dairy products of a country in order to maximize total revenue derived from the sales of those products

Address the optimal placement of facilities (from a set of candidate locations) in order to minimize the distance between a company’s facilities and its customers.

An electrical power generation problem (also known as a unit commitment problem) by selecting an optimal set of power stations to turn on in order to satisfy anticipated power demand over a 24-hour time horizon.

Solve a production planning problem and create an optimal production plan to maximize profit.

Solve a blending optimization problem with multiple raw materials combined in a way that meets the stated constraints for the lowest cost.

A problem of constructing a circuit using the minimum number of NOR gates that will perform the logical function specified by a truth table.

Solve a multi-period production planning problem to optimize mine production across a number of mines over a five-year period.

Identify which excavation locations to choose in order to maximize the gross margins of extracting ore.

A production planning problem, where decisions must be made regarding which products to produce, and which resources to use to produce those products.

Learn how to formulate and solve a multi-depot vehicle routing problem with time windows.

In this Car Rental problem you want to determine how many cars to own, where should they be located, and where to expand repair capacity to maximize profits.

This is an example of an economic planning problem that a country may face. The goal is to determine different possible growth patterns for the economy. This problem is formulated as a linear programming problem using the Gurobi Python API and solved with the Gurobi Optimizer.

The Efficiency Analysis example is a linear programming problem solved using the Gurobi Python API.

For a given a set of departments of a company, and potential cities where these departments can be located, determine the “best” location of each department in order to maximize gross margins.

A production planning problem, where decisions must be made regarding which products to produce, and which resources to use to produce those products.

Solve staffing planning problems, where choices must be made regarding recruitment, training, redundancy and scheduling of staff.

Solve a standard pooling problem, which is common in various industries including petrochemicals, wastewater treatment, mining, food and liquor processing, pharmaceuticals, heat exchanger networks, and supply chain operations.

The goal of the Traveling Salesman Problem (TSP) is to find the shortest possible route that visits each city once and returns to the original city.

A workforce scheduling optimization problem that deals with the arrangement of work schedules and the assignment of personnel shifts in order to cover the demand for resources that vary over time.

This model is an example of a Protein Folding problem formulated as a binary optimization problem using the Gurobi Python API and solved with the Gurobi Optimizer.

In this example, we consider a constraint of an integer programming model where all the decision variables in the constraint are binary, the goal is to find another constraint that is logically equivalent to the original constraint, but that has the smallest possible absolute value of the right-hand side.

This is an example of a Protein Comparison problem formulated as a quadratic assignment problem using the Gurobi Python API and solved with the Gurobi Optimizer.

This is an example of a vehicle routing problem formulated as a binary optimization problem using the Gurobi Python API.

This is an example of a capacitated vehicle routing problem and it is formulated as a binary optimization problem using the Gurobi Python API and solved with the Gurobi Optimizer

This is an example of a Yield Management problem formulated as a three-period stochastic programming problem using the Gurobi Python API.

In this example, we’ll solve a simple facility location problem: where to build warehouses to supply a large number of supermarkets.

In this example, we'll solve the problem of how to minimize the cost of laying underwater cables to collect electricity produced by an offshore wind farm.

This Workforce Scheduling demo addresses an important problem in the services industry: How to create shift schedules that maximize resource utilization.

In this example we'll solve the Traveling Salesman Problem. We'll construct a mathematical model of the problem, implement this model in Gurobi's Python interface, and compute and visualize an optimal solution.

Learn the key components of optimization modeling for mixed-integer programming (MIP) problems.

Utilizes supervised machine learning to predict basketball players fantasy scores from historical data and formulates an integer programming model to build the optimal lineup

A linear regression problem that minimizes the residual sum of squares subject to the constraint that the number of non-zero feature weights should be less than or equal to a given upper limit.

In this example we'll solve a simple covering problem: how to build cell towers to provide signal to the largest number of people.