Do you model optimization problems with Python and Pyomo? The Nextmv Pyomo integration makes it easier than ever to run, test, and deploy Pyomo models using Nexmv’s DecisionOps platform.
Change can be scary or energizing. But when it comes to developing and deploying algorithms that make routing, scheduling, and fulfillment decisions, ready-made testing tools tilt the balance in favor of energizing by building confidence.
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Work together on your decision apps and iterate faster with increased visibility across your entire team – from model management to experimentation. It’s as simple as inviting users to join your team in the Nextmv console.
Onfleet users can now optimize their routes with Nextmv’s extensible routing capabilities via our API integration that assigns tasks to workers based on business logic.
A quick intro to grouping stops together on a route for use cases ranging from food delivery to courier services and beyond.
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.
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Learn how to accelerate development of decision models that use Gurobi with tools for historical and online testing, run history, model management, and model collaboration.
See how the Nextmv Gurobi integration works in the Nextmv platform. With a few commands, you can apply DecisionOps to your decision modeling workflow to build, test, and deploy faster.
Test two order fulfillment algorithms that consider costs for distribution center handling costs and carrier selection. A new algorithm introduces a change to account for inventory capacity at a distribution center to increase efficiency and decrease food waste. How will costs change compared to the algorithm that does not?
See how to test two VRP decision algorithms (one that has a homogenous fleet of cold chain-ready vehicles and one that is a mixed fleet with cold chain and non-cold chain vehicles) that looks to compare total time on road values and other KPIs.
See how to test two shift scheduling algorithms. One algorithm increases the time between shifts to account for new labor laws compared to the other algorithm that does not. How do schedule overages change across the two?
Four decision algorithms, multiple experiments, one platform. Explore recently released batch testing capabilities for evaluating model changes to compare output metrics, KPIs, and prepare for acceptance and scenario tests.
Optimization model testing plays a key role in ensuring smooth yet improving business operations. But the testing universe is big and sometimes unwieldy. Learn how to get started with a unified decision model testing framework.
The next era of optimization isn't about building a better solver. It's about collaborative, opinionated tooling that empowers teams to move faster with less confusion and more access to the decision technology ecosystem.
Get started with Nextmv Cloud and Go to solve a vehicle routing problem and find optimized solutions for your vehicles and stops.
Get started with Nextmv Cloud and Python to solve a vehicle routing problem and find optimized solutions for your vehicles and stops.
Join our team for a techtalk video as they dive into the latest routing optimization features and chat about what’s coming soon to Nextmv in the decision automation space.
Get started with the Nextmv Onfleet integration in a step-by-step walkthrough for how to pull in tasks and workers via the Onfleet API, find optimized routes with Nextmv with extended features, and upload assignments back into Onfleet's platform.
Welcome to a tour of the Nextmv Cloud console! Learn how to get started with sample files for your routing use case. Optimize routes for delivery, distribution, and sourcing with Nextmv.Get started with Nextmv Cloud with our documentation.
When performing route optimization for use cases like delivery, distribution, and sourcing, the order in which the stops are serviced is critical. Learn how to use precedence to ensure your stops are visited in the correct sequence.
Balance vehicle usage by using constraints on aspects of route to limit maximum distance traveled, maximum time spent on route, and/or maximum number of stop serviced.
