I remember my first experience with optimization. I was taken to a guidance counsellor’s office at my local high school, where a special terminal was set up. This terminal was connected to a system that would allegedly try to find the “best” college for me. It asked many questions. Questions like, “Would you prefer a warm climate?” and “Would you prefer an academic setting with equal numbers of men and women?” Well, duh. Those were easy answers.
My goal was to attend one of the premier engineering schools in the US. I wanted MIT or CalTech or Stanford or Carnegie Mellon. I’d be happy with Rice. If my grades or scores weren’t good enough for the snooty super-competitive schools, I’d try for Rensselaer or Northeastern.
The system ended up choosing an entirely unsuitable school, evidently equally weighing my academic preferences and my social and geographic preferences.
What’s my point? Well, in a microcosm, this has been the essential problem with optimization. When you provide a “constraint” — and let’s be precise, here, the term really is “constraint” — an optimizer will not look outside that constraint for options. It cannot. It is a mathematical engine, and it can’t read your mind and figure out which is a “soft” requirement and which is a “hard” requirement. As far as it’s concerned, they’re all requirements, and, by whatever God you (don’t) believe in, it will find a solution that fits those requirements, if there is one.
That’s one reason why optimization has struggled to find its way.
I was listening to my wife talking to a survey telemarketer the other day. She said, “I really don’t have an opinion about Blue Cross’s responsiveness to patient needs. I’ve never had Blue Cross.” There was a pause. Then she said, “But how can I have an opinion on a 1 to 10 scale, if I’ve never used them?” There was another pause. She said, “OK, but ….” There was another pause. She sighed, and said, “OK, 5.”
What’s my point? Well, do you really know the answer to what kind of constraints you should impose on your optimization model? Or are you supplying an answer because you don’t know the answer, but you have to supply something? And after the optimization model has solved, can you remember all the places where you guessed, but you didn’t really know? What if you forgot one of those places? And what if that one guess caused the model to solve in a really non-optimal way (non-optimal from your perspective, not its)?
That’s another reason why optimization has struggled to find its way.
The breakthrough has come with what I’ll term “guided optimization”. If you hike in the White Mountains of New Hampshire, for example, you have a large number of excellent trails to choose from. Many of them are safe climbs that lead to outstanding views and vistas; but others lead up steep, often wet cliffs that are unsuitable for casual hiking. You need a guide; in this case, any of the excellent guide books from the Appalachian Mountain Club. In the case of optimization, your guide usually needs to be an experienced practitioner who can help you set up your model, show you how to move constraints to find inflection points in your model, and so on. (The good news is that lots of vendors provide guided services now, and it isn’t that expensive. Especially when you consider that optimization can be incredibly valuable.)
Companies that provide guided optimization services, like Trade Extensions, have enjoyed solid growth and have left a legacy of satisfied customers. You can always use optimization software on your own (Trade Extensions is no exception); but until you really understand what you’re doing, it can be unwise.
Optimization vendors have claimed for years that their systems are usable by novices. I don’t dispute that there are cases where this is true, and has been true. But for me, it’s a case of crying wolf: there have been so many claims, for so many years, with so many tears, that I’m solidly in the “get a guide” camp. I do hope, though, that optimization vendors will take additional steps to make guidance unnecessary. the doctor has assembled a pretty comprehensive list of what needs to happen.
At the end of the day, if you can’t do analysis yourself, you’re less likely to do it at all; which, as you’ll see in the next installment, is the theme of this series.