EVOPtimizer

This  software automates the most effective Evolutionary Operation (EVOP) technique.  Friendly and easy to use, you will be able to apply this software to difficult problems in only minutes.

After experimentation has been completed, and a process or design has been optimized, it is not uncommon for the experimentation team to consider the problem to be solved. In many cases, the optimum solution does not stay optimum; equipment changes as it ages, the composition of components change over time, and many other factors make small changes that affect the response variable. Evolutionary Operation (EVOP) is a method of addressing the constant small changes in a process in attempt to maintain an optimum level. Experimentation is carried out on a near continuous basis by introducing small changes to the system. One of the most popular methods of EVOP is sequential simplex optimization.

### Why EVOP

First of all EVOP is much easier to apply than traditional experimental techniques such as response surfaces, factorial designs and even Taguchi methods.  It is also more accurate.  A second order polynomial is the best approximation to the response surface other methods can make.  This assumption limits the accuracy of the final answer.  Another problem with conventional methods is the complexity with the number of factors.  The table below shows the minimum number of trials required for a response surface design as a function of the number of factors.

 Number of Factors Number of Trials 2 5 3 9 4 14 5 20

In addition, it usually takes several response surface designs (each modeling a smaller portion of the region) to obtain a satisfactory solution.

With sequential simplex optimization the number of trials in the initial simplex is k+1 where k is the number of experimental factors.  Optimization is achieved by evaluating the slope of the immediate area and moving in the best direction.

Another advantage of EVOP is the effect on current production.  The factor settings for the initial simplex can be set very close to current production settings.  This allows the initial experimental trials to be run with either no impact or a limited impact on either scrap or downtime.  Additional experimental trials improve existing production output over time.  EVOP is a great way to pursue continuous improvement!

Traditional experimental techniques are difficult to use when yield is the response variable.  How can conduct an experiment to reduce scrap for 1000 parts per million?  At least 1000 trials are required to expect to find one defect.  EVOP is perfect in this situation because the initial trials are conducted with factors setting very close to current production levels.  This allows an experimental setting to be replicated for weeks if necessary because there is either an improvement in scrap or a slight degradation.

### Compatibility

Required Operating System: Windows XP or later

### Try It!

Download the Demo Version.  The demo version is fully functional except you are limited to 2 factors and you cannot modify the initial simplex.  Click here to view a step by step example problem.

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