Page 179 - DCAP601_SIMULATION_AND_MODELING

Unit 10: Simulation of a PERT Network (II)

network of sub-projects. Two alternative strategies under central control and decentralized Notes
control for the simulation of time analysis have been presented.
In the simulation of a stochastic activity network (SAN), the usual purpose is to obtain point and
confidence-interval estimators of the mean completion time for the network. A new procedure
for using path control variants to improve the efficiency of such estimators. Because each path
control is the duration of an associated path in the network, the vector of selected path controls
has both a known mean and a known covariance matrix. All of this information is incorporated
into point- and interval-estimation procedures for both normal and non normal responses. To
evaluate the performance of these procedures experimentally, we compare actual versus predicted
reductions in point-estimator variance and confidence-interval half-length for a set of SANs in
which the following characteristics are systematically varied: (a) the size of the network (number
of nodes and activities); (b) the topology of the network; (c) the relative dominance (criticality
index) of the critical path; and (d) the percentage of activities with exponentially distributed
durations. The experimental results indicate that large variance reductions can be achieved with
these estimation procedures in a wide variety of networks.

Task Analyze how the size of network affects the simulation of an activity
network?
10.3 Computer Program for Simulation

A computer simulation, a computer model, or a computational model is a computer program,
or network of computers, that challenge to simulate an abstract model of a particular system.
Computer simulations have become a useful part of mathematical modeling of many natural
systems in physics (computational physics), astrophysics, chemistry and biology, human systems
in economics, psychology, social science, and engineering. Simulations can be used to explore
and gain new insights into new technology, and to estimate the performance of systems too
complex for analytical solutions.
Computer simulations vary from computer programs that run a few minutes, to network-based
groups of computers running for hours, to ongoing simulations that run for days. The scale of
events being simulated by computer simulations has far exceeded anything possible (or perhaps
even imaginable) using the traditional paper-and-pencil mathematical modeling. Over 10 years
ago, a desert-battle simulation, of one force invading another, involved the modeling of 66,239
tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple
supercomputers in the DoD High Performance Computer Modernization Program; a 1-billion-
atom model of material deformation (2002); a 2.64-million-atom model of the complex maker of
protein in all organisms, a ribosome, in 2005; and the Blue Brain project at EPFL (Switzerland),
began in May 2005, to create the first computer simulation of the entire human brain, right down
to the molecular level.
Simulation versus Modeling

Traditionally, shaping large models of systems has been via a mathematical model, which
attempts to find analytical solutions to problems and thereby enable the prediction of the
behavior of the system from a set of parameters and initial conditions.
While computer simulations might use some algorithms from merely mathematical models,
computers can combine simulations with reality or actual events, such as generating input
responses, to simulate test subjects who are no longer present. Whereas the missing test subjects
are being modeled/simulated, the system they use could be the actual equipment, revealing
performance limits or defects in long-term use by these simulated users.

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