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.
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.
Supply Network Design
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.
Offshore Wind Farming
In this example, you’ll learn how to solve an offshore wind power generation problem. The goal of the problem is to figure out which underwater cables should be laid to connect an offshore wind farm power network at a minimum cost. We’ll show you how to formulate a mixed-integer programming (MIP) model of this problem using the Gurobi Python API and then find an optimal solution to the problem using the Gurobi Optimizer.
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.
Marketing Campaign Optimization
Companies across almost every industry are looking to optimize their marketing campaigns. In this Jupyter Notebook, we’ll explore a marketing campaign optimization problem that is common in the banking and financial services industry, which involves determining which products to offer to individual customers in order to maximize total expected profit while satisfying various business constraints. You’ll learn how to formulate a mathematical optimization model of the problem (using machine learning predictive response models as parameters) and solve it using the Gurobi Optimizer.
Facility Location Problem
Facility location problems can be commonly found in many industries, including logistics and telecommunications. In this example, we’ll show you how to tackle a facility location problem that involves determining the number and location of warehouses that are needed to supply a group of supermarkets. We’ll demonstrate how to construct a mixed-integer programming (MIP) model of this problem, implement this model in the Gurobi Python API, and then use the Gurobi Optimizer to find an optimal solution.
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.
Logic Programming – 3D Tic-Tac-Toe
Try this logic programming example to learn how to solve the problem of arranging X’s and O’s on a three-dimensional Tic-Tac-Toe board so as to minimize the number of completed lines or diagonals. This example will show you how a binary programming model can be used to capture simple logical constraints.
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.
