Learn how to leverage DecisionOps to get your OR-Tools model into production faster. Then create decision workflows to automate steps in optimization pipelines, render custom visualizations, and more.
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Launch your OR-Tools model into production as a decision microservice with a simple copy/paste in Python using the Nextmv OR-Tools integration.
How do you feel about the decision model updates you ship to production? Acceptance and shadow testing are two ways to gain confidence across model performance for business KPIs and stability indicators. We’ll show you how.
Test two order fulfillment algorithms that consider costs for distribution center handling costs and carrier selection. A new algorithm introduces a change to account for inventory capacity at a distribution center to increase efficiency and decrease food waste. How will costs change compared to the algorithm that does not?
See how to test two VRP decision algorithms (one that has a homogenous fleet of cold chain-ready vehicles and one that is a mixed fleet with cold chain and non-cold chain vehicles) that looks to compare total time on road values and other KPIs.
See how to test two shift scheduling algorithms. One algorithm increases the time between shifts to account for new labor laws compared to the other algorithm that does not. How do schedule overages change across the two?
