Solving a workforce scheduling problem? Not sure where to start? We walk through the basics and cover what you need to know about optimizing and automating shift scheduling.
Solvers are a source of an invisible kind of magic. When they’re absent, we notice. So what are they, how do you use one, and why do you need one?
Building decision models into binaries is a beautiful thing. It eliminates a lot of sticky deployment processes and gets you to production faster.
.jpeg)
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?
.png)
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.
