The traveling salesman problem is a classic challenge of finding the shortest and most efficient route for a person to take given a list of destinations. This is one of many real-world optimization problems that companies encounter. How should they schedule product distribution, or promote product bundles, or define sales territories? The answers to these questions constantly change because business environments constantly change.
The company Nextmv helps solve these problems with production-ready, commercial tools for solving optimization problems and simulating models with real company data. Their tool Hop encodes optimization strategies for dynamic environments. Hope can be deployed to routing, scheduling and assignment problems in multiple industries like on-demand delivery, e-commerce, and IT infrastructure management. Their tool Dash is a commercial-grade simulation engine that provides an environment to “A/B test” models online with real data.
In this episode we talk to Carolyn Mooney, CEO at Nextmv. Carolyn was previously a Lead Systems Engineer at Grubhub, and a Decision System Analyst at Zoomer before that. We discuss optimization problems throughout different industries, machine learning strategies for solving them, and go into detail about how Nextmv helps companies become more profitable and efficient.
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