nextmv Docs


We believe that algorithms should be like any other software. You shouldn't need a wizard to automate delivery routes or a scientist to build a shift scheduler. We give developers the legos to make and experiment with decision models without a PhD.

This user manual should get you working with the Nextmv platform quickly. Reading it will help you understand its design, and how to go from business requirements and operational data to deployed models in production.

Our tools are intended to scale up and down to meet different business needs. This guide is not exhaustive. We recommend reading through the package documentation to get to know individual components.

Our Platform

The Nextmv platform is an opinionated set of developer tools for rapidly building optimization and simulation models in production environments. While Hop, the decision modeling and optimization system, and Dash, the discrete event simulator, provide different functionality, they share common designs and behaviors.


Hop is a decision modeling and optimization tool built for developers. It helps you automate decisions like routing, scheduling, and assignment.

Hop helps you put automation in place quickly, iterate on it easily as your requirements change, and automatically gather evidence along the way so you know if it's working the way you want. This minimizes time spent on infrastructure and maximizes modeler impact.


HopM is a collection of standardized, reusable high performance models. These models leverage many of the same fundamentals as Hop, but are already structured and tuned for high volume use cases.

Each of these models follow modern layered architecture techniques such that each component can be combined, interchanged, or modified as needed for a particular use case. For example, a capacitated vehicle routing problem with time windows (cvrptw) model will build and extend a the capacitated vehicle routing problem (cvrp) model.


Dash is a fast discrete event simulation engine built for the cloud. It helps you predict and compare the outcomes of complex systems. Dash makes it easy to model user behavior and quickly estimate outcomes using real data.