Stop reinventing the wheel. Discover how a centralized hub for mathematical optimization models is accelerating innovation and bridging the gap between academia and industry.
In the world of mathematical optimization, a common frustration unites business leaders, researchers, and modelers alike: the immense difficulty of finding the right model for a specific problem. Valuable models are buried in academic papers, internal company documents, and isolated GitHub repositories. This fragmentation leads to duplicated efforts, hindered research, and a significant barrier for businesses seeking to leverage optimization for decision-making.
What if there was a single, trusted source for the global optimization community? A place to not just find models, but to understand, compare, and build upon them?
Today, we are proud to introduce that solution: The AMPL Optimization Model Repository.
The AMPL Model Repository is a centralized, cloud-based platform designed to be the definitive library for mathematical optimization models. It goes beyond a simple database by providing a standardized framework for documenting, categorizing, and accessing models from a vast array of industries and domains.
Think of it as a “Wikipedia meets GitHub” specifically for optimization, designed to foster collaboration and accelerate progress for everyone from students to Fortune 500 companies.
Our repository is built on principles of clarity, collaboration, and universality.
Powerful, User-Friendly Discovery: Quickly find models using an intuitive interface that filters by critical criteria such as:
Domain & Subdomain (e.g., Energy > Petroleum Refining)
Model Type (e.g., MILP, NLP, Stochastic Programming)
Modeling Language & Solver (e.g., Python with Gurobi, AMPL with CPLEX)
Objectives & Constraints (e.g., Maximize Profit, Capacity Limits)
Standardization for clarity: Every model is classified under a unified structure with 20+ attributes, ensuring you can easily understand its purpose, mechanics, and applicability without wading through inconsistent documentation.
Rich, Actionable Model Cards: Each model includes everything you need to assess its fit:
Detailed Descriptions of the problem and approach.
Author information and citations for proper attribution.
Implementation examples and links to code.
Visualizations and data formats (JSON, Excel inputs/outputs).
Seamless Integration: The repository can be embedded into any webpage or platform with just two lines of code, ensuring everyone has access to the latest models without complex installations.
This repository is designed for the entire community:
For Business Users & Analysts: Discover how optimization is applied in your industry. Gain structured insights to guide strategic decision-making and identify opportunities for operational improvement without needing a PhD in operations research.
For Academics & Students: Accelerate research and learning. Quickly find benchmark models, compare different approaches to a problem, and promote reproducibility and collaboration within the academic community.
For Modelers & Developers: Efficiently manage and share your work. Showcase your expertise, contribute to the community’s knowledge base, and avoid starting from scratch on every new project.
To ensure true understanding, every model is meticulously tagged with a rich set of attributes, including its Domain, Subdomain, Constraints, Objectives, Modeling Language, Solver, and Adoption Level (e.g., “Industry Prototype”). This granularity allows for precise comparison and ensures you can trust a model’s applicability to your specific challenge.
We are dedicated to building a platform by the community, for the community. We prioritize:
Transparency: Clear documentation of model assumptions, limitations, and author credits.
Scalability: The repository is designed to grow, adapting to new tools, languages, and methodologies as the field evolves.
Knowledge Sharing: Acting as a hub for best practices to foster innovation and bridge the gap between theoretical research and industrial application.
Technical Development Team
