Blog

Getting optimization solutions into the hands of stakeholders faster at Grubhub

How Grubhub’s data science team uses Nextmv to accelerate model development, ship models as microservices, and build trust with business users.

How to scale logistics planning with optimization models

What if you could explore 100s or 1,000s of possible plans in seconds instead of 10s of plans in days or weeks? Optimization models make this possible all while keeping humans in the loop to ensure quality and build trust.

Operationalizing Python decision models: configurable options, simple I/O, custom logging, and more

If you’re building decision models in Python, our Python SDK and decision science platform make the development process faster (and easier) so you can get your model safely into production.

In conversation with the HiGHS project developers

What is HiGHS? How is it used for MIP solving? Who’s using HiGHS? And what’s next for this open source project? We spoke with the creators of the HiGHS project to find out.

Observability & decision science: Monitoring optimization model performance and more

When decision models power real-life operations, any sort of model performance failure is a nightmare. Learn why observability in the operations research space is often a challenge – and how to give your team more visibility into model performance with DecisionOps.

Simulate “what if” questions for decision models with scenario testing and Nextmv

What if order volume increases 4x? What if I changed shift length? What’s the best model formulation? Efficiently play out different scenarios under realistic conditions before committing to a plan using Nextmv’s scenario testing capabilities.