Decision models are sophisticated algorithms that power revenue, sustainability, and efficiency goals through optimized planning. But integrating them into software stacks is not always straightforward.
Solve optimization problems with Hexaly? The Nextmv Hexaly integration provides a new way to efficiently run, test, and manage Hexaly decision models with Nextmv’s DecisionOps tools and infrastructure.
Do you solve decision science problems with Gurobi? The Nextmv Gurobi integration accelerates how you run, test, and deploy decision models running Gurobi with Nexmv’s DecisionOps platform.
CI/CD is how you ship higher quality code to production faster. But with decision science models, there’s a twist. We’ll look at the role of decision quality, specific testing tooling, and problem drift on CI/CD for decision science models.
Many awesome projects and people underpin the optimization world. We're making mvs to surface more open source model code for real-world impact and better support individual decision modelers.
Using a Hexaly model, see how to use Nextmv ensemble definitions to automatically select the best plan from many solutions based on your specified criteria.
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.
A look at how Nextmv’s ML/OR connectors better optimize the plans generated by decision models through streamlining the incorporation of machine learning outputs such as forecasts. Plus, avocados are involved.
What approaches are available to decision scientists and operations researchers to incorporate more randomness and uncertainty into their models? We explore this, ML + OR, and stochastic optimization with Nextmv and Seeker.
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.
In the on-demand logistics space, ML and OR are colliding more frequently with practitioners generating demand forecasts that feed into shift scheduling models that feed into vehicle routing models. How can we benefit from their combined value more often?
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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.
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.
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.
In the beginning there was linear programming. It spawned decades of similarly-shaped solver offshoots. Decision diagrams break with this classic paradigm and offer new opportunities to solve common optimization problems.
Is optimization a solved problem? How does it fit into modern business models such as on-demand delivery? What does it mean to model like an operator? We’ll ask Dr. Hoffman these questions and more.
Nextmv is removing the roadblocks for going from optimization problem definition to production environment. This makes optimization easier for operations researchers and more accessible to developers. See what this looks like and watch a demo.
