Testing optimization models improves workflows, increases stakeholder buy-in, and helps teams deploy to production safely and quickly. But what are the steps to make testing repeatable and scalable?
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What is HiGHS? How is it used for MIP solving? Who’s using HiGHS? And what’s next for this open source project? We spoke with the creators of the HiGHS project to find out.
When decision models power real-life operations, any sort of model performance failure is a nightmare. Learn why observability in the operations research space is often a challenge – and how to give your team more visibility into model performance with DecisionOps.
What if order volume increases 4x? What if I changed shift length? What’s the best model formulation? Efficiently play out different scenarios under realistic conditions before committing to a plan using Nextmv’s scenario testing capabilities.
We examine the value of treating decision models as engineered software components and how to approach decision modeling with an adaptable process.
Accelerate development of your Python decision models – from completely custom models to those built using popular modeling tools – with features for testing, deploying, managing, and collaborating.
