I have a great idea for a new reality adventure television series.
The basic premise is simple. Contestants are blindfolded and driven to a starting location on the side of a mountain. When the race begins, each contestant must find a path to the base of the mountain as quickly as possible. The blindfolds make it impossible for contestants to detect the contours and obstacles in the landscape.
When contestants are working alone, the strategies they can use are limited. If the terrain is smooth, like a rolling pasture, then contestants might find a successful path by taking small steps in several different directions, and then choosing the direction that leads downward. When the contestant feels that path starting to flatten out or trend upward, she knows it’s time to stop and choose a new downward direction. Repeating this process many times should lead each contestant to the bottom of the nearest valley, which depends on the starting location. The first one to the bottom wins! Continue reading
There are common stages that most companies pass through when improving their product design process. Each new level promotes greater efficiency and predictability of their process, as well as higher performance and innovation of their products. It is possible to skip one or more steps to reap faster rewards, but the most important thing is to keep moving higher. Which stage represents the optimization maturity of your organization?
Stage 1. Physical prototyping: build and test
A trial-and-error approach to building and testing a myriad of hardware prototypes makes it too expensive to consider many design alternatives. Continue reading
Yes, this is an odd title for a blog post that is meant to promote optimization. But this opinion is expressed more often than you might think, especially among engineers who have tried to apply classical optimization technology to their challenging design problems.
So, what is causing smart people to form this opinion? I believe there are four types of experiences that cause people to lose faith in optimization:
1. The optimized solution was not as good as expected.
Those big improvements you hoped for were not realized. But did you include all the key design variables and allow them to vary broadly enough to really improve the design? Often we are taught to reduce the number and range of the design variables to allow for the limitations of classical optimization algorithms. Modern search strategies don’t have these limitations; they can efficiently explore broader and more complex design spaces, with a higher chance of finding superior solutions.
Conducting more design iterations can lead to higher-quality designs and increased innovation. So, when faced with a tight design schedule, the goal of many organizations is to iterate faster. But in most cases, performing faster manual design iterations doesn’t make the design process more productive.
Consider the consequences of maximizing iteration throughput for a typical manual design process. Let’s assume a simple, but familiar, scenario in which each iteration involves the following steps:
- Create a CAD model of the geometry,
- Build a math model to predict performance,
- Execute the math model, and
- Interpret its results.