Personal blog on early retirement, a little traveling, and analytics projects. I write about what I learn as I am learning it.
At some point during this project I stepped back and looked at what we’d built.
Three files. A working web application. Deployable for $7 a month. Zero web development experience going in.
That felt worth writing down.
When you convert an optimization model from Excel to standalone Python, you expect to do some work. You expect to rewrite the data loading, restructure the variable definitions, test the output. What you don’t expect is for the model to fail in three distinct ways, each one caused by something the Excel version was handling silently without you knowing it.
That’s what happened here. Three bugs. All real. All the kind that would have stayed invisible forever if the model had stayed in the spreadsheet.
Continue reading “Three Bugs That Would Have Stayed Hidden in Excel”PuLP is a Python library for writing optimization models. MILP stands for Mixed Integer Linear Program. CBC is an open-source solver. Together, they’re what makes the Staff Scheduler work — and together, they represent something I find genuinely interesting: the fact that problems that used to require expensive commercial software and specialized hardware can now be solved on a laptop, for free, in a few seconds.
Let me explain what’s actually happening under the hood.
Continue reading “PuLP, MILP, and CBC: The Alphabet Soup Behind Your Schedule”A while back I wrote an introduction to the shortest superstring problem and promised two follow-ups: one showing the greedy algorithm I wrote in VBA, and another showing how I bolted optimization on top to do better than greedy alone. Then I did what I usually do with “future work” and let it sit. Here, finally, is that follow-up — the whole thing in one post, because this is really the story of how I solved the shortest superstring problem in Excel with two stages of the same machine: a VBA pass that generates candidates, and an optimization model that picks the best of them.
Continue reading “How I Solved the Shortest Superstring Problem in Excel”
This is the last post in my accidental Fourier-Motzkin trilogy (part one: the method; part two: the baseball lineups), and it’s about the bill coming due. Because when you use elimination to throw variables out of a model, the mathematics is very clear about what you keep — and very quiet about what you lose. I found out exactly what I’d lost the first time DraftKings rejected a lineup my spreadsheet swore was legal. The story runs straight through a beautiful piece of combinatorics called Hall’s marriage theorem, so we’ll pick that up along the way.
Continue reading “Hall’s Marriage Theorem Meets the DraftKings Entry Form”