Kind-of-Lost

A hotel front desk never closes, which means someone senior has to be reachable at 3am just as much as at 3pm — even though the person who can actually approve a comp or handle an angry guest at 3am is a different kind of role than the night auditor working the desk. Housekeeping runs on its own tight window, and maintenance mostly doesn’t need covering overnight at all.

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My generalized staff scheduler is a small optimization problem: eight people, a week of half-hour slots, about five thousand binary variables. By modern standards that is tiny. So when it started taking minutes to solve — or worse, returning nothing at all — my first reaction was disbelief. This post is the story of CBC vs HiGHS on that exact model, and the four fixes that actually mattered. If your PuLP + CBC model is grinding, some of these will probably apply to you too.

Continue reading “Why a Tiny Scheduling MILP Took Minutes: CBC vs HiGHS”

The staff scheduler I built for that Fiverr client did exactly one job. It knew about his clients, his courses, his equipment, his week. It was a good staff scheduling model for precisely one business on Earth. Every sheet name, every constraint, every assumption was his. When I put it online I knew the truth: nobody else could use it without hiring me to rebuild it. So I rebuilt it once, properly, for everyone. This is how the generalization went.

Continue reading “From One Client’s Rules to Any Team: A General Scheduler”