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Since 1985, research at Oklahoma
State University's CCiMe has been exploring alternative
approaches to the modeling and simulation of complex systems.
Research conducted in the 1990s has resulted in a prototype
advanced modeling environment, which is generic in nature and
can be used for many problem specific approaches to systems
analysis (e.g. simulation, queueing and Petri net analysis).
A very encouraging development
within the prototype environment is the demonstration of the
feasibility of creating logical decision modularity among the
decision elements of a modeled system. This is equivalent to
"plug compatible decision elements", which can be replaced as
desired in any location in the model. This accomplishment
represents a major conceptual breakthrough. The prototype
environment permits the distinct and separate specification of
physical elements (machines, material handlers, etc.), decision
elements, and data/information elements. Furthermore, models
can be constructed at varying levels of detail, and can be
retrieved, reconfigured, and reused as desired.
The approach to modeling and simulation described
above is clearly revolutionary, not evolutionary. Thus, it
represents a fundamental paradigm shift, not only in the
construction of models, but in their utilization and maintenance
within an operating environment. One of the significant
features of this approach is the integration of analytical and
simulation methodologies within a single software
environment. The ability to perform quick "rough cut" analysis
using analytical techniques and detailed analysis using
simulation within the same descriptive model will dramatically
reduce the time needed for planning and designing or
reconfiguring complex manufacturing systems. In addition, the
modeling environment will include a comprehensive set of
decision support tools that provide on-line, near real-time
assistance to operations managers at various levels.
Early AME research was funded by
grants from the AT&T Foundation and the Oklahoma Center for the
Advancement of Science and Technology (OCAST). Further
development of the advanced modeling environment was funded by a
grant from the National Science Foundation (NSF).
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