The science of better decisions, in plain English
Field notes on operations research, optimization, and AI — grounded in real work and published research, written to be useful before it's clever.
The math behind a smaller fleet: how optimization removes vehicles without cutting service
Most fleets are sized by experience, and experience over-provisions. Here is the operations-research reframe that removes vehicles while every trip still runs on time.
ReadOperations research or machine learning: which one actually makes the decision?
Everything gets called “AI” now. But prediction and decision are different jobs — and picking the wrong tool is why so many AI projects stall before they change anything.
ReadDecisions that survive contact with reality: a plain-English guide to robust optimization
The “optimal” plan often fails, because the numbers it was built on were never certain. Robust optimization is how you decide well when the inputs can move.
ReadDemand forecasting a retailer can actually plan on
A single-number forecast is a trap. Real forecasting gives you a range, is honest about what it doesn’t know, and only pays off when a decision hangs off it.
ReadAfter the buses: cutting legal driver duties and a fair roster
Finding the minimum fleet is the easy half of transit optimization. Turning those vehicle blocks into legal driver duties and a fair weekly roster is the hard, expensive, human half.
ReadGrounded AI: real answers from your documents, without the hallucinations
In the enterprise, a fluent wrong answer is worse than no answer. Grounded AI ties every response to your own material — with citations, and the discipline to say “I don’t know.”
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