Link and test logistics models for demand forecasting, shift scheduling, and vehicle routing

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.

How to run your OR-Tools model as an automated decision service

Ready for your OR-Tools model to have real-world impact? Create an operational decision app in minutes using the Nextmv OR-Tools integration.

How to pull Nextmv run data into Google Sheets

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.

Building a docs site with Next.js and Markdoc

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.

How to build a custom order fulfillment decision model with MIP and Nextmv

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.

Operationalizing HiGHS-based MIP models and Q&A with project developers

What is HiGHS? How is it used for MIP solving? And how can you accelerate the impact of decision models that use open source projects? We’ll cover all of this with a live walkthrough, demo, and a Q&A with the HiGHS project maintainers.

Getting started with DecisionOps – Live Nextmv workshop

In this hands-on workshop designed for operations researchers (decision scientists), developers, and data scientists, participants will get a guided introduction to DecisionOps via the Nextmv platform.

Vehicle activation penalties to encourage vehicle efficiency (vehicle routing, VRP)

Learn how to use vehicle activation penalties to encourage vehicle efficiency. This is sometimes known as prioritizing backhaul when going back to a depot.

Multiple pickups before a dropoff for vehicle routing problem (VRP)

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.

Model continuous depot visits for vehicle routing problem (VRP)

Learn how to model continuous moves for a vehicle going back and forth from a depot.

OR-Tools, VROOM & Nextplot: Open source vehicle routing and visualization

Analyzing solutions and updating models can be tricky when you’re using new or multiple modeling tools and solvers. In this techtalk, we’ll demo Nextplot with two sample VRP apps to visualize model input, output, and more.

Link and test logistics models for demand forecasting, shift scheduling, and vehicle routing

Follow this step-by-step tutorial to go from a forecasted demand to optimized routes using OR-Tools, HiGHS, and Nextmv.

Operationalizing Java-based OR-Tools decision models

Learn how to create a decision service with your Java OR-Tools model using Nextmv. Deploy an existing model or accelerate the development of a new Java model with testing, CI/CD, and more.

Comparing decision models in operational environments with switchback testing

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.

Nextplot demo: An open source route visualization tool for JSON

Learn how to use Nextplot to visualize points, routes, and more on a map.

Operationalizing Python-based Pyomo MIP decision models

Learn how to build, test, and deploy Pyomo mathematical optimization models faster with Nextmv, featuring pre-bundled solvers for CBC and GLPK. Create a new model or integrate an existing one to accelerate its development with DecisionOps tooling.

Switchback testing decision models: Demo using Nextmv

Use Nextmv to compare two decision models operating in production while accounting for network effects

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.

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.

Create and run a low-code, SaaS routing app

Launch and run your own routing app with a library of configurable constraints to fit your use case.

Getting started with vehicle routing problems (VRPs) – and testing, too!

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.

How to perform an acceptance test for decision algorithms

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.

Deploy a HiGHS MIP knapsack model with a custom endpoint

Access your HiGHS model remotely. Deploy your model as an app to Nextmv Cloud in minutes.

Best practices for customizing your model in 30 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.

Route optimization for food, beverage, and less-than-truckload (LTL) delivery

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.

Route optimization for package and parcel delivery

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.

Deploy a customizable, decision optimization app in 5 minutes

See how to build, run, and deploy a custom decision model to production in a few minutes.

Creating a custom distance or duration matrix to use with Nextmv

Learn how to use custom distance or travel time matrices for routing with Nextmv.

Solving a VRP with varying worker types

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.

Getting started with Nextmv

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.

How to optimize route operations in multiple market locations

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.