Build, test, and deploy an OR-Tools MIP model in Python

Learn how to solve mixed integer programming (MIP) problems with Google’s OR-Tools for use cases like scheduling, order fulfillment, packing and more. Then promote an updated model to production using CI/CD.

Forecast, schedule, route: 3 starter models for on-demand logistics

Automating on-demand logistics operations for scale, customization, and iteration is easier than you might think. Learn how to build, test, and deploy models for demand forecasting, shift scheduling, and route creation.

Operationalizing Google OR-Tools models

Learn how to integrate a new or existing OR-Tools model into production systems using Nextmv and its infrastructure, testing capabilities, and collaboration features to create a repeatable workflow to production.

Assigning workers to shifts with the Nextmv Shift Scheduling app

With the Nextmv Shift Scheduling app, you can start automating shift scheduling decisions in minutes.

Deploying an OR-Tools model to production

Launch your OR-Tools model into production as a decision microservice with a simple copy/paste in Python using the Nextmv OR-Tools integration.

Determining decision model readiness using shadow tests and acceptance tests

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.