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Follow this step-by-step tutorial to go from a forecasted demand to optimized routes for delivery and similar use cases. Create and customize decision apps using OR-Tools, HiGHS, Pyomo, and more.
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.
I retired our on-call scheduling spreadsheet — and you can too. Here’s how I built a custom decision model that generates and sends optimized schedules to the PagerDuty API.
Learn how to use vehicle activation penalties to encourage vehicle efficiency. This is sometimes known as prioritizing backhaul when going back to a depot.
Learn how to configure your vehicle routing problem (VRP) to have multiple pickups precede a dropoff. In this example, learn how to set two pickups at two different locations precede a dropoff stop for a pickup and delivery problem.
Learn how to model continuous moves for a vehicle going back and forth from a depot.
Create robust and interactive decision apps with AMPL as the optimization layer, Nextmv as the DecisionOps layer, and Streamlit as the UI and visualization layer. Learn how with a facility location example.
When you’re ready to have a candidate model make true operational decisions, it’s time for switchback testing. Kick off an experiment and analyze how your new decision model measures up to your current model in production.
With Nextmv, you can customize an optimization model for your use case without wading into linear inequalities. From creating your own value function to adding custom constraints, learn best practices for representing business logic as code.
