Many operations research and decision science practitioners start their optimization modeling journey with open source projects such as HiGHS. What is HiGHS? What role does it play in the MIP ecosystem? And how can we accelerate the real-world impact of HiGHS-based decision models?
In this session, we’ll explore these questions alongside the HiGHS project developers, Julian Hall and Ivet Galabova. We’ll also cover how to accelerate operationalizing HiGHS-based MIP models using DecisionOps tooling. We’ll demonstrate this through price optimization and scheduling examples that use HiGHS — from standing up a custom API endpoint backed by scalable compute, to using scenario testing to help with model iteration, running acceptance tests to set defined expectations, and hooking into Git-based CI/CD workflows.
We’ll also save time for discussion about the recent HiGHS workshop, what’s next in the HiGHS community, and the role of open source solvers in the growth of optimization as a discipline.
Key topics
- An overview of HiGHS
- Running, testing, and managing HiGHS-based decision models
- Examples with shift scheduling and price optimization
- Q&A with HiGHS project maintainers
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