Optimization is often highlighted in supply chain contexts – routing, scheduling, inventory, and other tangible, visible problems. This post explores a few use cases in computing and software.
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We explore continuous improvement for a Google OR-Tools decision model using testing techniques at each stage of the development lifecycle and GitHub.
It’s time to understand the behavioral impacts of a new decision model under real-world conditions compared to a production model. Switchback testing enables this, helping build confidence in a new model’s rollout in a safe and measured way.
From configuring model options to performing experiments on subscribed apps, there’s a whole lot more you can do in the Nextmv console to accelerate the development of your decision algorithms.
A look at how one startup is supporting sustainable and resilient city design by optimizing urban delivery logistics — starting with dispatch apps designed for eco-friendly operators.
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In a few clicks, you can spin up, configure, and run a Nextmv decision app that assigns workers to flexible or fixed shifts. Use a unique API endpoint to automate shift scheduling for delivery, healthcare, education, retail use cases, and more.
