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.
Last year, we focused on accelerating deployment and testing for decision models. 2024 looks to focus on operating and monitoring models with more platform integrations and greater visibility and control across your development lifecycle.
NVIDIA GPU-accelerated decision optimization has elevated the conversation around decision intelligence. Watch a Q&A conversation with the NVIDIA cuOpt team to learn more.
“I wouldn’t pair these products.” “How much better is this optimized schedule than mine?” Human review and feedback is part of any decision workflow...
Historian and philosopher Hannah Arendt once said, “Storytelling reveals meaning without committing the error of defining it.” While good stories of operations research and data science can come from practitioners of all kinds...
The operations research and decision science space has a diverse portfolio of open source projects, including Pyomo and HiGHS. Recently, new momentum is building around project adoption...
“I’d like help deploying this decision model. Does this .ipynb work? Or would you prefer .zip?” If you work on decision science projects, it is possible you’ve asked or been asked this question...
Good. Fast. Cheap. Pick two, they say. But (true to form) decision intelligence teams strive to maximize for all three — project success often hinges on it. Balancing these objectives often comes down to a team’s agility ability. What does this look like? And how is it put into practice?
Two operations research PhDs with varied real-world experiences explore skillsets, actions, and considerations for entering into and practicing in OR and decision science in industry analytics settings.
Integrated, holistic optimization approaches are critical for long-term success — without them your project will fail. Learn what these workflows look like through a batch production scheduling example.
“What is your AI strategy? How are you investing in AI? Where are you incorporating AI into your everyday workflows?” Before you think “GenAI” and “LLMs”, have you considered optimization?
Learn how to leverage data and AI for better operational decisions.
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.
