Data scientists, engineers, computer scientists, economists, and in general, professionals with a background in mathematical modeling and a basic knowledge of Python.
- Illustrate the broad applicability of mathematical optimization.
- Show how to build mathematical optimization models.
These modeling examples are coded using the Gurobi Python API and distributed as Jupyter Notebooks.
These modeling examples illustrate important capabilities of the Gurobi Python API, including adding decision variables, building linear expressions, adding constraints, and adding an objective function. They touch on more advanced features such as generalized constraints, piecewise-linear functions, and multi-objective hierarchical optimization. They also illustrate common constraint types such as “allocation constraints”, “balance constraints”, “sequencing constraints”, “precedence constraints”, and others.
- 3D Tic-Tac-Toe
- Agricultural Pricing
- Car Rental
- Cell Tower
- Constraint Optimization
- Curve Fitting
- Customer Assignment
- Decentralization Planning
- Economic Planning
- Efficiency Analysis
- Electrical Power Generation
- Facility Location
- Factory Planning
- Fantasy Basketball
- Farm Planning
- Food Manufacturing
- Intro to Mathematical Optimization Modeling
- Linear Regresion
- Logical Design
- Lost Luggage Distribution
- Manpower Planning
- Market Sharing
- Marketing Campaign Optimization
- Milk Collection
- Mining
- Music Recommendation
- Offshore Wind Farming
- Opencast Mining
- Price Optimization
- Protein Comparison
- Protein Folding
- Refinery
- Standard Pooling
- Supply Network Design
- Technician Routing and Scheduling
- Traveling Salesman
- Workforce Scheduling
- Yield Management
These modeling examples are distributed under the Apache 2.0 license, (c) copyright 2019 Gurobi Optimization, LLC