Abstract
Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques useful for developing, improving, and optimizing processes. Applications of RSM can be found in e.g. chemical, engineering and clinical sciences. Still, there does not seem to be an established code of practice for the automated application of RSM in the field of simulation optimization. In this paper our aim is to find the best settings for an automated RSM procedure when there is very little information about the objective function. We present a framework of the RSM procedures for finding optimal solutions and emphasize the use of both stopping rules and restart procedures. Various versions of the RSM algorithms are compared on a number of test functions, including a simulation model for cancer screening. The results show that considerable improvement is possible over the proposed settings in the existing literature. Accordingly, we give general recommendations on the application of automated RSM algorithms in simulation optimization.
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