AMPL is a modern optimization platform used by organizations in energy, logistics, finance, and advanced analytics to design and deploy large-scale optimization models integrated directly into production systems.
AMPL is a modern optimization platform used by organizations in energy, logistics, finance, and advanced analytics to design and deploy large-scale optimization models integrated directly into production systems.
AMPL is designed for large-scale decision systems where optimization models involve millions of variables, complex discrete and nonlinear constraints, and evolving business rules that quickly exceed the capabilities of spreadsheets or ad-hoc scripting. These models are often embedded directly into operational workflows, connected to enterprise data pipelines, analytics environments, and production software systems. By separating model logic from solver technology, AMPL provides the flexibility to evaluate and deploy models across leading commercial and open-source solvers without rewriting model code. The result is a stable modeling platform capable of supporting mission-critical optimization systems that evolve and operate reliably over years of development and real-world use.
From modeling to deployment, AMPL provides the infrastructure required to build and operate large-scale optimization systems. Define complex models with a purpose-built modeling language, connect them to modern data and analytics environments, solve them with leading commercial solvers, and deploy them into production decision systems.
AMPL’s purpose-built modeling language allows teams to define complex optimization models clearly and maintainably, separating model logic from data and solver configuration.
Design models that scale from early experimentation to production deployment while maintaining full control over constraints, objectives, and solver behavior.
See also
AMPL integrates directly with Python, allowing optimization models to be embedded within data science workflows and engineering systems.
Use Python to orchestrate data pipelines, manage experiments, and build optimization-driven applications while leveraging AMPL’s high-performance modeling and solver interfaces.
Our amplpy interface allows developers to access the features of AMPL from within Python.
AMPL provides seamless access to leading commercial and open-source optimization solvers. Switch solvers instantly without modifying model logic, enabling teams to evaluate performance, scale workloads, and adapt to evolving infrastructure requirements.
AMPL’s solver interface and presolve capabilities ensure consistent model behavior while leveraging each solver’s advanced algorithms.
AMPL MP is the solver interface that connects AMPL models to a wide range of optimization solvers, ensuring compatibility, high performance, and continuous support as solver technologies evolve.
AMPL models integrate directly into operational systems, enabling organizations to embed optimization within planning tools, analytics pipelines, and real-time decision platforms.
Deploy models across cloud environments, containerized infrastructure, or custom applications while maintaining full control over solver execution and data flows.
$200B+
revenue influenced by optimization models built with AMPL
$500M+
operational costs reduced through AMPL-based systems
50+ countries
organizations using AMPL
40+ years
of optimization modeling innovation
Organizations across energy, logistics, finance, and infrastructure rely on AMPL to power complex optimization systems in production. From power grid operations to global supply chains, AMPL enables teams to build, deploy, and scale optimization models that support critical operational decisions.
Optimizing power grid planning
Grid operators use Hitachi’s GridView platform, powered by AMPL, to model large-scale electricity markets and transmission systems. The system solves optimization models with millions of variables, enabling planners to evaluate generation schedules and grid investments.
Impact
Used by 30+ power companies
Hundreds of energy analysts running grid optimization models
Large MILP models solved in ~10 minutes
Optimizing sales territory assignments
With millions of customer accounts and a rapidly growing sales organization, Dropbox needed a scalable way to assign accounts to sales representatives. Using AMPL, the team built an optimization model that automates territory design by balancing workloads, prioritizing high-value accounts, and integrating directly with Salesforce data and Python-based analytics.
Impact
Optimization models with up to 10,000 assignment variables per region
Balanced workloads across sales teams
Automated territory planning integrated with CRM and analytics systems
Optimizing fast-fashion inventory allocation
Zara operates in a high-velocity retail environment where inventory decisions must be made quickly as new sales data arrives. Using AMPL-based optimization models, Zara determines how inventory should be distributed across stores by combining demand forecasts, store-level inventory data, and warehouse constraints to generate optimized shipment decisions.
Impact
1,500+ stores supported by allocation optimization
Approximately 15,000 optimized inventory decisions per week
Faster, data-driven distribution planning across global retail operations
AMPL’s consulting team works with organizations to design, build, and deploy large-scale optimization systems. From early architecture decisions to production deployment and performance tuning, our experts help accelerate development and ensure reliable operational outcomes.
Work with AMPL experts to design scalable optimization systems, including model architecture, solver selection, data integration, and deployment workflows.
Students, researchers, and early-career engineers use AMPL to learn optimization modeling and build systems similar to those used in industry. Through free academic licenses, interactive notebooks, and modern Python workflows, AMPL helps the next generation of optimization practitioners develop the skills needed to design and deploy real operational decision systems.
Universities and students receive free access to AMPL and leading commercial solvers for teaching, coursework, and research projects.
Learn optimization by building real models using AMPL and Python. Explore a large library of Colab notebooks, Streamlit apps, and example models covering real industry problems.
Use amplbot and tools like ChatGPT to explore models, understand formulations, and experiment with optimization techniques while learning AMPL.
Create an account to instantly get started or contact us to design a custom package for your business.
AMPL works with the tools already used in your Python workflows, data pipelines, solver infrastructure and cloud deployment environments.
