ENBIS-8 in Athens

21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008

Analysis of Computer Experiments with Multiple Noise Sources

23 September 2008, 16:30 – 16:50


Submitted by
Christian Dehlendorff
Christian Dehlendorff, Murat Kulahci and Klaus Kaae Andersen
DTU Informatics, Technical University of Denmark
With the current advances in computing technology, computer and simulation experiments are being used more and more frequently to study complex systems for which physical experimentation is usually not feasible. Such experiments are often considered to be without experimental noise. For certain applications such as in health care, this is however not adequate as the variations in the operating conditions in these applications are an important concern for the decision makers. In this study a simulation model of an orthopaedic surgical unit is considered. The discrete event simulation (DES) model describes the individual patient’s flow through the unit and is developed in collaboration with the medical staff at Gentofte University Hospital in Copenhagen.
The overall objective in this study is to minimize the patient waiting time from the time he/she enters the ward until the exit. Multiple outcomes are considered simultaneously; both quality requirements and outcomes related to the study objective. Often the quality requirements and the objectives are inversely related requiring some sort of trade-off solution.

The simulation model consists of two sources of noise coming from variations in the environmental factors (uncontrollable factors in the physical system) and from changes in the seed controlling the random number generation process embedded in the simulation model. Methods for evaluating and splitting the variation into separate components in a meta-model, i.e. a fixed effect corresponding to the controllable settings, a random effect corresponding to the environmental variables and a residual term corresponding to the changes in seed, are presented. The resulting meta-model is used in minimizing the waiting time while satisfying the quality requirements since using the simulation model directly is computationally infeasible. The optimal settings are evaluated under different environmental settings to quantify the robustness of the solutions and to identify influential environmental factors.
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