2022 was a big year: the next-gen Nextmv platform, multi-paradigm solving, INFORMS, curling in Montreal. 2023 has even more goodness in store.
.jpg)
Traditional decision optimization and automation setups can take many months and several teams to ship to production. What if it didn’t have to be that way?
Solvers help us tackle big problems. But how well do you understand the solver technology that you have? Did you build it? Buy it? Will it scale with your operations?
It’s new and yet old. It bridges data science and business operations. And it’s an emerging piece of critical infrastructure for realizing a more efficient, responsive, and predictable world.
From software development to operations research to the classroom, Professor Thiago Serra has experienced the world of optimization through many lenses. We chatted with him to learn more.
NVIDIA GPU-accelerated decision optimization has elevated the conversation around decision intelligence. Join a live Q&A 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.
