Month: October 2010

Made popular by the Seinfeld television series, the Soup Man restaurant in New York City demands that customers know what kind of soup they want before arriving at the counter. Signs are prominently displayed, stating the rules in several languages:

FOR THE MOST EFFICIENT AND FASTEST SERVICE

Failure to follow these rules may result in the harshest of penalties — no soup for you!

Like the Soup Man restaurant, the optimization process is not well suited to those who don’t have a clear set of goals in mind.  Continue reading →

The word parsimonious means thrifty, economical, frugal, and sometimes even stingy. It is an unusual word to use when describing optimization, but it is meaningful here in two ways.First, the purpose of optimization is to minimize a function. The intent is not a meager reduction, but absolute minimization. This seems pretty stingy, but in a useful way.

Second, and of greater interest here, the process of optimization must be efficient, economical, and thrifty. That is, finding an optimized solution to a problem should take as little of your time and resources as possible. Unfortunately, in many real-world applications, time is the largest barrier to realizing the true value of automated optimization.

In theory, you should be able to find an optimized solution whenever you have a good system analysis model and an appropriate search algorithm.  But if the model requires hours or even days of CPU time for each design evaluation, and the algorithm requires a large number of evaluations, then the total time required to reach that optimized solution may turn out to be completely impractical.

Let’s consider the cost factors involved. The total CPU time needed to find an optimized solution is defined this way:

where NSOL is the number of optimization solutions performed, and the expression inside the brackets represents the total search time per iteration of the optimization solution.

Based on this formula, there are only four ways you can reduce the solution time for an optimization study. You can

Continue reading →

High school tryouts. College recruiting. Pro draft day. At every level of athletics, coaches face a tough pre-season decision. Select player A, a better than average athlete who has worked diligently for many years to maximize his potential under the tutelage of top coaches. Or choose player B, whose present skill level is not quite as high but who has greater raw athletic ability, is coachable and has real potential to be a superstar – a trait that coaches call “upside.”

A similar situation occurs in product development when selecting between two competing design concepts. A more optimized version of concept A may appear better than a version of concept B that has not been optimized. But concept B may have a lot more potential for improvement, a bigger upside.

The performance of a single example of a concept is not usually a good measure of the concept itself.

How optimized is concept A? What level of performance could be attained by concept B? We seldom know the answers to these questions prior to performing an optimization study.   Continue reading →