Increasingly, OR practitioners are seeking to incorporate more real-world uncertainty into decision models instead of only relying on deterministic optimization approaches. In this interview, we’ll explore this topic through the lens of Seeker, a new stochastic optimization solver.
Whether you’ve already built a decision model or are just getting started, developing your optimization project on the Nextmv platform will give you the framework, testing tools, and ease of integration required to prove the value of your decision model.
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.
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.
The ops-ification of disciplines such as software development, machine learning, and security aims to increase efficiency and reduce risk. For decision science and operations research — a discipline built on efficiency — it’s no different.
How do optimization teams get decision models live into business processes faster as managed services? We explore this through the lens of dedicated DecisionOps workflows.
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.
The CEOs and founders of two startups sit down with Carolyn Mooney to discuss logistics and automation, navigating the evolving world of AI technology, and the benefits of efficiency and sustainability.
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.
