Personal blog on early retirement, a little traveling, and analytics projects. I write about what I learn as I am learning it.
Operations research in practice — vehicle routing, staff and shift scheduling, project scheduling and resource leveling, and the free optimization tools I built out of twenty years of Excel models. How the math actually works, and where it breaks.
Linear programming is a mathematical method for optimizing the allocation of resources — maximizing profit, minimizing cost, that kind of thing. I’ve built dozens of these models over 20+ years in operations research, and one concept I lean on constantly, and that clients almost never intuit on their own, is the shadow price: what a change in one constraint does to your optimal solution.
The six W’s are six questions — who, what, when, where, why and how — that you ask about a problem before you try to solve it. They come out of journalism, where a reporter has to establish the facts of a story, and they got adopted into continuous improvement work for exactly the same reason: you cannot fix something you haven’t described properly.
I spent most of my working life as an operations research analyst, building models for manufacturing and supply chain problems. And the honest lesson from all of it is that the modeling was rarely the hard part. Getting a straight answer to these six questions was the hard part.
Continue reading “The 6 W’s Explained: Who, What, When, Where, Why, How”
Mathematical programming is a field of mathematics that deals with the optimization of complex systems and processes. It has a rich history that dates back to the 1940s when George Dantzig, an American mathematician, developed the simplex method for solving linear programming problems. Since then, it has grown into a vast and dynamic discipline that has found applications in various fields such as finance, transportation, engineering, and operations research.
In this blog post, I will explore the origins of mathematical programming, provide an example of a problem that can be tackled and list some classical problems. I will also discuss some of the software products used in mathematical programming, both open-source and commercial.
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”
I was introduced to the shortest superstring problem by a request I got on Fiverr. The request turned out to be a waste of time but I really enjoyed learning about it.
The shortest common superstring problem aims to find a string with a minimal length that contains every string in a given set.
