There isn’t a simple guideline. Which one of the design methods you use will depend on what it is that you want to do and the kinds of variables you have.
For example:
1. Are the variables of interest continuous (or can they be treated as continuous), are they nominal, or are they a mixture of the two?
2. Are you trying to assess mixtures?
3. Are there known combinations of independent variables that either cannot be physically run or if running them would result in something you know will be other than you will need for the final results of an experiment?
4. What can you afford in terms of time/money/effort?
5. Are there restrictions with respect to randomizing the run order of your design which will require you to block your design on certain variables?
6. Is this going to be an initial investigation with a number of variables whose impact is: well defined, not well understood, of some interest because we suspect it might be important, included out of curiosity, or maybe a mix of all of the preceding.
7. Is this a situation where you are fairly certain of the effect of the variables of interest and are interested in seeing if there is anything better within the current parameter space?
8. Do you expect the relationship between the variable level and the output to be something other than a simple linear one? If so do you have the time/money/effort/desire to check for all possible curvilinear behavior or only just a select few?
9. Do you think there might be some synergistic effects (interactions) between certain variables? If so which ones?
etc.
Once you have the answers to questions like these then you can press on with a discussion concerning the merits of the various choices for DOE. So, if you have something in mind please provide the answers to the above questions and either I or someone else will be happy to provide additional information/advice.
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