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When: Sunday, April 16, 2023 from 1:00 PM - 2:45 PM
Where: Cottonwood 4
Accessibility: Free and open to all conference registrants
Presenter: Tiffany Bogich
Staff: Ryan O'Neil, Carolyn Mooney, Joe Melliere, Chris Keyser, Ashley Simone, Haley Eshagh
Nobel Prize winner Dr. John Mather once said, “If you do not test it, it will not fly.” These words ring true in the world of testing optimization apps. How does one model perform compared to another? How do solutions change as you add or remove constraints? Based on a number of scenarios, how do I grow or adapt my operations? Does a model or optimization solver perform the way I’d expect before deploying it to production? How does a model or solver perform when faced with real-world data? And this is the short list of questions. However, setting up, implementing, and maintaining a testing framework is often a bespoke buildout or relegated to an aspirational project to get to one day.
This workshop will walk you through different types of testing and get you hands-on with an optimization testing toolkit that aims to make testing table stakes for any optimization workflow.
Recommended pre-workshop steps:
1. Create a free Nextmv account at https://cloud.nextmv.io/signup
2. Download and install Nextmv’s developer tools at https://cloud.nextmv.io/template/routing
When: Monday, April 17, 2023 from 9:00 AM - 9:50 AM
Where: Cottonwood 11
Accessibility: Free and open to all conference registrants
Presenter: Ryan O'Neil
Staff: Carolyn Mooney, Tiffany Bogich, Joe Melliere, Chris Keyser, Ashley Simone, Haley Eshagh
When optimization technology works well, it feels magical. But it is not magic. Good decision optimization is both art and science. But the path to useful solutions that impact business operations is often fraught with roadblocks and dead ends -- from model definition to solver setup to testing to deployment. So what's the answer?
The next era of optimization isn't about building a better solver. It's about collaborative, opinionated tooling that empowers teams to move faster with less confusion and more access to the decision technology ecosystem. The result: a decision optimization workflow that makes it possible to take a locally developed decision model and run it in a managed remote endpoint in minutes. This improves OR ops by reducing the time teams need to spend on infrastructure and maximizing the time they can spend on model development, testing, and iteration and ultimately derive more value out of their existing optimization stack.
