Benchmarking Optimization Tools for Scale: AIMMS vs. Pyomo & JuMP
Large-scale optimization plays a vital role in solving complex problems across industries. As models scale in size and complexity, performance becomes critical — especially in enterprise settings where fast turnaround is a must.
With the release of AIMMS 24.6.1 in late 2024, we introduced a redesigned model generation engine, purpose-built for large-scale use cases. To evaluate its real-world impact, we asked Deanne Zhang to lead a comparative study on performance.
The study benchmarks AIMMS against two popular open-source tools — Pyomo (Python) and JuMP (Julia) — using anonymized logistics workforce models featuring 1M+ variables and constraints. It focuses on model generation time and total execution time, offering a practical lens on tool performance at production scale.
The results are insightful — and relevant for anyone navigating large, mission-critical optimization workflows.