The main topic of this thesis is design and analysis of computer and simulation
experiments and is dealt with in six papers and a summary report.
Simulation and computer models have in recent years received increasingly more
attention due to their increasing complexity and usability. Software packages
make the development of rather complicated computer models using predefined
building blocks possible. This implies that the range of phenomenas that are
analyzed by means of a computer model has expanded significantly. As the
complexity grows so does the need for efficient experimental designs and analysis
methods, since the complex computer models often are expensive to use in terms
of computer time.
The choice of performance parameter is an important part of the analysis of
computer and simulation models and Paper A introduces a new statistic for
waiting times in health care units. The statistic is a measure of the extent
of long waiting times, which are known both to be the most bothersome and
to have the greatest impact on patient satisfaction. A simulation model for
an orthopedic surgical unit at a hospital illustrates the benefits of using the
Another important consideration in connection to simulation models is the design
of experiments, which is the decision of which of the possible configurations
of the simulation model that should be tested. Since the possible configurations
are numerous and the time to test a single configuration may take minutes or
hours of computer time, the number of configurations that can be tested is limited.
Papers B and C introduce a novel experimental plan for simulation models
having two types of input factors. The plan differentiates between factors that
can be controlled in both the simulation model and the physical system and factors
that are only controllable in the simulation model but simply observed in
the physical system. Factors that only are controllable in the simulation model
are called uncontrollable factors and they correspond to the environmental factors
fluencing the physical system. Applying the experimental framework on
the simulation model in Paper A shows that the effects of changes in the uncontrollable
factors are better understood with the proposed design compared
to the alternative and commonly used methods.
In papers D and E a modeling framework for analyzing simulation models with
multiple noise sources is presented. It is shown that the sources of variation
of the simulation model can be divided in two components corresponding to
changes in the environmental factors (the uncontrollable factor settings) and
to random variation. Moreover, the structure of the environmental effects can
be estimated, which can be used to put the system in a more robust operating
The interpolation technique called Kriging is the topic of Paper F, which is
a widely applied technique for building so called models-for-the-model (metamodels).
We propose a method that handles both qualitative and quantitative
factors, which is not covered by the standard model. Fitting the final Kriging
model is done in two stages each based on fitting regular Kriging models. It is
shown that this method works well on a realistic example such as a simulation
model for a surgical unit.