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Explore our modeling examples below, access them via GitHub, or download the entire collection. You can also check out examples for other programming languages, or dive into our functional code. MIP developers: Be sure to check out ticdat.

Introductory: Tutorial Example

This tutorial assumes that you know Python and that you have a background in a discipline that uses quantitative methods.

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Beginner

These modeling examples assume you know Python and have some knowledge about building mathematical optimization models.

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Intermediate

These modeling examples assume that you have some knowledge about building mathematical optimization models. In addition, you should know Python and be familiar with the Gurobi Python API.

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Advanced

These modeling examples 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.

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Intermediate

Agricultural Pricing

Try this example to learn how to use mathematical optimization to tackle a common, but critical agricultural pricing problem:  Determining the prices and demand for a country's dairy products in order to maximize total revenue derived from the sales of those products. You will learn how to model this problem as a quadratic optimization problem using the Gurobi Python API and solve it using the Gurobi Optimizer.

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Introductory

Airline Planning After Flight Disruption

Weather events are a major threat to the airline industry. This notebook walks through the optimization problem of deciding which flights to operate and which flights to cancel after a weather disruption.

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Introductory

Avocado Price Optimization

This example optimizes avocado prices to maximize revenue using a quadratic program. The relationship between price and demand is modeled using linear regression.

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Intermediate

Best Feature Selection for Forecasting

In this example, you will learn how to perform linear regression with feature selection using mathematical programming. We'll show you how to construct a mixed-integer quadratic programming (MIQP) model of this linear regression problem, implement this model in the Gurobi Python API, and generate an optimal solution.

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Beginner

Cell Tower Coverage Problem

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

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Beginner

Combining Machine Learning and Optimization Modeling in Fantasy Basketball

Fantasy sports has turned into a mainstream activity over the last ten to twenty years with leagues now working with popular fantasy sports sites as official partners. If you’re not familiar with fantasy sports, the goal is to select players from a slate of real games to fill out a virtual lineup.

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Advanced

Constraint Optimization

If you are looking to improve your modeling skills, then try this tricky constraint optimization problem. We'll show you how to model this problem as a linear programming problem using the Gurobi Python API and solve it using the Gurobi Optimizer.

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Beginner

COVID-19 Facility Capacity Optimization

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

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Beginner

Curve Fitting

Try this Jupyter Notebook Modeling Example to learn how you can fit a function to a set of observations. We will formulate this regression problem as a linear programming problem using the Gurobi Python API and then solve it with the Gurobi Optimizer.

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Intermediate

Customer Assignment

Sharpen your mathematical optimization modeling skills with this example, in which you will learn how to select the location of facilities based on their proximity to customers. We'll demonstrate how you can construct a mixed-integer programming (MIP) model of this facility location problem, implement this model in the Gurobi Python API, and generate an optimal solution using the Gurobi Optimizer.

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Advanced

Decentralization Planning

Ready for a mathematical optimization modeling challenge? Put your skills to the test with this example, where you'll learn how to model and solve a decentralization planning problem. You'll have to figure out – given a set of departments of a company, and potential cities where these departments can be located – the "best" location for each department in order to maximize gross margins.

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