What is the relationship between price, profit, and waste? Let’s find out using a Statsmodels ML regressor, a Gurobipy optimizer, Plotly charts, scenario testing, and a slick decision workflow built and run in Nextmv.
Accelerate development of your Python decision models – from completely custom models to those built using popular modeling tools – with features for testing, deploying, managing, and collaborating.
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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.
Ready for your OR-Tools model to have real-world impact? Create an operational decision app in minutes using the Nextmv OR-Tools integration.
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.
Learn how to leverage DecisionOps to get your OR-Tools model into production faster. Then create decision workflows to automate steps in optimization pipelines, render custom visualizations, and more.
Deploy and run a Statsmodels ML regressor, Gurobipy price avocado optimizer, and end-to-end decision workflow on Nextmv via a Jupyter Notebook on Google Colab. Plus, visualize results using Plotly and perform a scenario test to determine relationship between price and profit.
Interested in Hexaly to solve optimization problems? This techtalk covers how to get started with Hexaly in Nextmv, use features for scenario testing, best plan auto-selection, model management, and a Q&A with Hexaly’s CEO.
In under ten minutes, we demo how to deploy and run your local Python decision model from a Jupyter Notebook as a fully featured decision app on the Nextmv platform for simpler collaboration and a smarter workflow.
Go from a local decision model to a remotely hosted decision application (with API endpoints) that you can easily share, test, and confidently promote to production. See the model-to-app workflow using a Jupyter Notebook and Nextmv in under 30 minutes.
Deploy, run, and test your HiGHS model from a notebook without translating to different formats like .mps or .lp files.
Deploy, run, and test a VRP model from a notebook without translating to different formats like .mps or .lp files.
Deploy, run, and test your Gurobi model from a notebook without translating to different formats like .mps or .lp files.
If you develop decision models in Python, this presentation will save you time (and the added effort of building and maintaining DecisionOps tools). Accelerate development of your optimization models with features for testing, deploying, managing, and collaborating.
Simulate scenarios to answer "what if" questions with your decision model.
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.
In this step-by-step video, we’ll walk you through deploying a Python OR-Tools traveling salesperson problem (TSP) model using the Nextmv Python template.
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.
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.
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.
Follow this step-by-step tutorial to go from a forecasted demand to optimized routes using OR-Tools, HiGHS, and Nextmv.
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.
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.
Learn how to use Nextplot to visualize points, routes, and more on a map.
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.
Use Nextmv to compare two decision models operating in production while accounting for network effects
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.
