or-tools-series-staff Archives

What’s Next: 20 More Models Waiting in Excel

The staff scheduler took about 20 years to build.

That’s not as dramatic as it sounds. The MILP model — the math, the constraints, the logic — that came together during the Fiverr engagement described in the first post of this series. What took 20 years was accumulating enough Operations Research experience to know what the model needed to look like. The actual build, once I sat down with the problem fully understood, was fast.

The web app took a few days. The deployment took an afternoon, plus one failed attempt that taught me about gunicorn.

From Localhost to the Internet: Deploying for $7/Month

The app was working. On my laptop. Which is the same as not working, for most purposes.

If the goal was to keep it to myself — test it occasionally, tinker with it, update the model when I felt like it — a working local copy would be enough. But that’s not what I built it for. The point of converting these models to web apps is that they can run anywhere, for anyone, without requiring someone to have Python installed and know how to use a terminal.

So: deployment.

Three Files. One Web App. Zero Web Development Experience.

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.

Three Bugs That Would Have Stayed Hidden in Excel

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.

PuLP, MILP, and CBC: The Alphabet Soup Behind Your Schedule

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.