Gurobi Optimizer
| Company type | Private |
|---|---|
| Industry | Mathematical Optimization, Prescriptive Analytics, Decision Intelligence |
| Founded | 2008 |
| Headquarters | Beaverton, Oregon |
Key people | Dr. Zonghao Gu, Dr. Edward Rothberg, and Dr. Robert Bixby |
| Website | https://www.gurobi.com/ |
Gurobi Optimizer is a prescriptive analytics platform and a decision-making technology developed by Gurobi Optimization, LLC. The Gurobi Optimizer (often referred to as simply, “Gurobi”) is a solver, since it uses mathematical optimization to calculate the answer to a problem.
Gurobi is included in the Q1 2022 inside BIGDATA “Impact 50 List” as an honorable mention.[1]
History
[edit]Dr. Zonghao Gu, Dr. Edward Rothberg, and Dr. Robert Bixby founded Gurobi in 2008, coming up with the name by combining the first two initials of their last names.[2] Gurobi is used for linear programming (LP), quadratic programming (QP), quadratically constrained programming (QCP), mixed integer linear programming (MILP), mixed-integer quadratic programming (MIQP), and mixed-integer quadratically constrained programming (MIQCP).[3][4]
In 2016, Dr. Bistra Dilkina from Georgia Tech discussed how she uses Gurobi in the field of computational sustainability, to optimize movement corridors for wildlife, including grizzly bears and wolverines in Montana.[5]
In 2018, The New York Times reported that the U.S. Census Bureau used Gurobi to conduct census block reconstruction experiments, as part of an effort to reduce privacy risks.[6]
Since 2019, Gurobi is used by National Football League (NFL) to build its game schedule each year.[7][8]
In 2020, Gurobi has partnered with GE Digital GE Grid Solutions, the University of Florida, and Cognitive Analytics on a project for planning and scheduling day-ahead electricity supply.[9]
In 2021, DoorDash used Gurobi, in combination with machine learning, to solve dispatch problems.[10]
In 2023, Air France used Gurobi to power its decision-support tool, which recommends optimal flight and aircraft assignments and can take constraints like fuel consumption and an aircraft’s flying hours into account.[11][12]
References
[edit]- ^ Gutierrez, Daniel (2022-01-10). "The insideBIGDATA IMPACT 50 List for Q1 2022". insideBIGDATA. Retrieved 2023-04-26.
- ^ INFORMS. "Gurobi Optimization". INFORMS. Retrieved 2023-04-26.
- ^ Analytics, Opex (2019-11-13). "Optimization Modeling in Python: PuLp, Gurobi, and CPLEX". The Opex Analytics Blog. Retrieved 2023-04-26.
- ^ "Using the Gurobi Optimizer Solvers on the Eagle System". nrel.gov. Retrieved 2023-04-26.
- ^ "Computing cost-effective wildlife corridors". Mongabay Environmental News. 2016-11-11. Retrieved 2023-04-26.
- ^ Hansen, Mark (2018-12-05). "To Reduce Privacy Risks, the Census Plans to Report Less Accurate Data". The New York Times. ISSN 0362-4331. Retrieved 2023-04-26.
- ^ "Meet the minds behind the 2019 NFL schedule: Mike North and Charlotte Carey". NFL. Retrieved 2023-04-26.
- ^ "An Introduction to the National Football League Scheduling Problem using" (PDF). Carnegie Mellon University.
- ^ "High-Performance Computing Helps Grid Operators Manage Increasing Complexity | PNNL". pnnl.gov. 11 September 2020. Retrieved 2023-04-26.
- ^ Shenwai, Tanushree (2021-08-23). "How DoorDash Uses Machine Learning ML And Optimization Models To Solve Dispatch Problem". MarkTechPost. Retrieved 2023-04-26.
- ^ Lin, Belle. "Startups Want to Help Airlines Prevent Tech Meltdowns". WSJ. Retrieved 2023-06-23.
- ^ Lin, Belle. "Southwest Meltdown Shows Airlines Need Tighter Software Integration". WSJ. Retrieved 2023-06-23.