Design and Analysis of Simulation Experiments (DASE) focuses on
statistical methods for discrete-event simulation (such as queuing
and inventory simulations). In addition, the book discusses DASE
for deterministic simulation (such as engineering and physics
simulations). The text presents both classic and modern statistical
designs. Classic designs (e.g., fractional factorials) assume only
a few factors with a few values per factor. The resulting
input/output data of the simulation experiment are analyzed through
low-order polynomials, which are linear regression (meta) models.
Modern designs allow many more factors, possible with many values
per factor. These designs include group screening (e.g., Sequential
Bifurcation, SB) and space filling designs (e.g., Latin Hypercube
Sampling, LHS). The data resulting from these modern designs may be
analyzed through low-order polynomials for group screening and
various metamodel types (e.g., Kriging) for LHS.
Design and Analysis of Simulation Experiments is an
authoritative textbook and reference work for researchers, graduate
students, and technical practitioners in simulation. Basic
knowledge of simulation and mathematical statistics are expected;
however, the book does summarize these basics, for the readers'
convenience. In addition, the book provides relatively simple
solutions for (a) selecting problems to simulate, (b) how to
analyze the resulting data from simulation, and (c) computationally
challenging simulation problems.
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