Here you will find real-world uses of the BARON solver, including robot gearboxes, lithium supply chains, power grids, disaster relief logistics, mixture separation, and more.
KAIST DRCD Lab/Techeblog
Gearbox design for hopping robots
KAIST researchers used BARON to solve a mixed-integer nonlinear optimization problem for gearbox gear teeth, designing a compact hollow-shaft reducer that met torque constraints and enabled reliable dynamic hopping robot performance.
Tianqi Lithium Energy Australia/IGO
Lithium supply chain network design
A case study in Mexico applied BARON to solve the optimization model in their study of circular-economy indicators in the lithium supply chain. They chose BARON because it found good solutions faster than other solvers. Using BARON enabled them to efficiently evaluate trade-offs between economic and environmental objectives in planning and choose more sustainable supply chain strategies.
Justin Sullivan/Getty Images
Power system scheduling
Researchers in India used BARON (in GAMS) to plan which power plants should run and when, while accounting for electricity lost as heat on transmission lines, on three test grids (two standard IEEE systems and one larger 75-bus utility system). BARON consistently outperformed other tools by finding schedules that cost less to operate while balancing supply and demand and staying within operating limits.
Andy Dean Photography/Shutterstock
Disaster relief with trucks and drones
Researchers used BARON to plan disaster aid deliveries using a mix of trucks, drones, and boats. Instead of only choosing the fastest overall route, they also worked to keep any one shelter from waiting much longer than the others, making the distribution more fair. BARON helped them quickly produce strong delivery plans so they could be used for real-time response.
Cang Hai/Pexels
Liquid mixture separation
Princeton researchers used BARON (in GAMS) to design an efficient membrane-based system for separating mixed liquids. BARON solved a complex mixed-integer nonlinear planning problem in minutes and identified the best possible design, helping them determine the optimal separation setups.
Zhang, Y., & Sahinidis, N. V./The Optimization Firm
Advances in global NLP and MINLP
This paper explains how BARON has improved over the past decade through major algorithmic upgrades that enable it to solve nonlinear and mixed-integer problems faster and more reliably. It also shares benchmark results showing that BARON 2025 consistently beats other solvers on standard test sets.
Best MINLP Solver
BARON has ranked at the top in public benchmarks year after year. Those benchmarks consistently show BARON reaches feasible solutions and proves optimality faster than competing solvers.
Technical support
BARON support is handled by BARON's developers (PhD-level) who work on real-world models and enterprise deployments. They can advise on algorithmic or software features that can materially improve performance in your BARON runs.
Updates and new features
The development team works continuously to provide several BARON releases each year. Each release includes performance improvements and new capabilities driven by user feedback, so you can solve larger models faster.