Every time you run your app, Nextmv captures metadata that provides insight into how the app is performing. Use a simple script to pull that run data into Google Sheets to analyze and visualize KPIs over time.
We built a docs site that references code samples from external files to avoid copy-paste errors, makes it easier to manage code updates over time, and allows for separate testing processes to be run to validate referenced code. Here’s how.
This post explains what order fulfillment is, considerations for model constraints and objective functions, and walks you through building a custom optimization model, step by step.
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.
Looking to solve a vehicle routing problem? Need to implement a custom constraint or value function? Learn how to create a custom routing model to represent your business logic.
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.
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.
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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.
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.
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Launch and run your own routing app with a library of configurable constraints to fit your use case.
Planning efficient routes for your vehicle fleet helps you save on operational costs. Learn how to automate the creation of optimized routes that take business rules into account like capacity, precedence, time windows, and more.
Define the metrics that matter to your organization, run an acceptance test, and get easy-to-share results that guide your team down the path to production with confidence.
Access your HiGHS model remotely. Deploy your model as an app to Nextmv Cloud in minutes.
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.
From tight delivery windows to refrigeration controls, route optimization models for food, beverage, and LTL delivery often require customization and rapid deployment to keep pace with business operations. Learn how to use Nextmv for this use case.
Efficiently scale your delivery volume and service areas without adding stress to your operators or drivers. We’ll show you how to use Nextmv to automate and optimize routing: start with a pre-built decision model, customize your model to fit your needs, and deploy it to production.
See how to build, run, and deploy a custom decision model to production in a few minutes.
Learn how to use custom distance or travel time matrices for routing with Nextmv.
Learn how to build a custom model using our routing template to minimize costs while accounting for workers who are paid either by the hour or by task.
Build and run complete decision optimization models in minutes for vehicle routing, scheduling, packing, and Sudoku. With a few commands, you're ready to solve.
Whether you operate in multiple market locations or want to expand into new ones, simple scenario testing can help you make decisions about vehicle fleet size, composition, and capabilities.
