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5
Modeling and Simulation
Research and Development Topics
Recent advances in modeling and simulation (M&S) technologies
make them increasingly appealing as a means of improving commercial
manufacturing and defense acquisition. However, in order for these M&S
technologies to support the desired applications in commercial
manufacturing and defense acquisition, additional research and
development (R&D) is needed. In its statement of task, the committee was
asked to investigate emerging M&S technologies, assess ongoing efforts to
develop them, and identify gaps that would have to be filled in order to
make these emerging technologies a reality. The committee rephrased this
task and sought to determine those M&S topics requiring R&D in order for
M&S to be effectively used in commercial manufacturing and defense
acquisition. The topics requiring R&D were identified by the committee on
the basis of the expertise of its members and information obtained from
expert briefings. In addition, the committee surveyed literature calling for
M&S R&D. The committee grouped these topics into four broad
categories: (1) modeling methods, (2) model integration, (3) model
correctness, and (4) standards, which are discussed in the sections that
follow.
77
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MODELING AND SIMULATIONIN MANUFACTURING
MODELING METHODS
Lack of adequate modeling methods is one of the most serious
shortfalls in using M&S (MORS, 2000~. In order to maximize the potential
of M&S technologies for commercial manufacturing and defense
acquisition, basic research must be undertaken to improve understanding of
modeling methods and characteristics, including scalability,
multiresolution modeling, agent-based modeling, semantic consistency,
modeling complexity, fundamental limits of modeling and computation,
and uncertainty.
Scalability
Scalability is the attribute of a system's architecture that pertains to
the behavior and performance of the system as the size, complexity, and
interdependence of its elements or applications increase. Difficulties in
dealing with large-scale software systems are well documented (NRC,
2000~. Techniques that work for small systems often fail markedly when
the scale is increased significantly. To be upwardly scalable, a system must
assure consistency in both the functionality and the quality of the services
it provides as the number of its users increases indefinitely. To scale by a
million, an application's storage and processing capacity would have to be
able to grow by a factor of 1 million just by adding more resources (NRC,
2000~. This implies that as a system expands or as performance demands
increase, the underlying architecture must support the ability to
reimplement the same functionality with more powerful or capable
infrastructure, for example, replacing a single server with a high-
performance server farm.
Traditional modeling and simulation have focused on microlevel
components rather than on macrolevel integration of these components.
However, with the advent of large-scale systems such as extended
enterprises and distributed mission training, it is necessary to develop
approaches for designing scalable M&S system architectures, including
process specifications, linguistic support, granularity, and levels of
abstraction to support system architecture design. This effort includes
modularization, interconnectivity, and integration platforms as well as the
standardization of application programs, automatic installation of modules,
and verification. Metrics for such designs include robustness, reliability,
flexibility, and the ability of the system to adapt dynamically to changing
conditions. Several levels of architectural scalability are illustrated in
Figure 5-1.
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RESEARCHAND DEVELOPMENT TOPICS
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ente~se
into
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conn~i~,it~~ t ~ Node ~pa~y ~
Enterprise A~hits~re Level
1~ 1
,,
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~:~
~del~ng and Simulation ~Ye'
Figure 5-1 Levels of architectural scalability.
79
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I R*~-t~rm~ event ct,anne!
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_ Node capability 13-
IT- Infrastru~L`~e Level
Current and foreseeable trends are to employ object-oriented
technology to enable scalability attributes. In object-oriented terms, the
scalability problem can be stated as designing a system with the
appropriate interface definitions that allow the implementations behind the
interfaces to be upgraded from single objects to multiple coordinated
objects or to objects of more capable classes. Abstraction, modularity, and
layering are the basis of such interface design concepts (Messerschmitt,
2000~. Scalability designs must live within existing resources in
communications bandwidth and computing power available from the
underlying computing and network technologies. A practical approach to
scalability also requires consideration of interoperability in order to
address the problems of data heterogeneity that are due to a lack of
accepted standards and the current multiplicity of approaches (IMTI,
2000~.
Multiresolution Modeling
"Multiresolution modeling" and/or "multiresolution simulation" is
defined as the representation of realworld systems at more than one level
of resolution in a model or simulation, respectively, with the level of
resolution dynamically variable to meet the needs of the situation. R&D
into multiresolution modeling has been recommended (NRC, 19971. It is
considered especially important for SEA because acquisition programs will
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MODELING AND SIMULATION IN MANUFACTURING
need to move up and down the resolution hierarchy and use the proper
level of models and simulations to support iterative trade-off analyses
(Ewen et al., 2000~. In addition, multiresolution simulation has the
potential to improve the scalability and flexibility of simulation
applications. A related concept is "multiviewpoint simulation." In this case,
simulation takes place at a single level of resolution, but the execution
events and results are presented at different levels of resolution, or
viewpoints, as appropriate to the needs of the user.
Significant unresolved issues in implementing multiresolution
models, however, account for the need for research in this area. A number
of multiresolution simulations have been implemented (Stober et al., 1995;
Franceschini and Mukherjee, 1999), but that work has approached the
problem largely from an experimental and practical point of view. As yet,
no complete and coherent theoretical framework exists for multiresolution
models, although some work leading toward such a framework has been
completed (Davis, 1993; Franceschini and Mukherjee, 1999~. Some
problematic issues arise in multiresolution models, including maintaining
consistency between levels of resolution when aggregation and
disaggregation operations occur (Davis, 1993; Franceschini and
Mukherjee, 1999), dealing with "chain" or "spreading" disaggregation
(Petty, 1995), allowing interactions between objects at different levels of
resolution, and preserving consistency during reengagements. Some work
has been done on each of these issues, but more is required. In addition,
multiresolution modeling affects the architecture of the simulations that
use it by requiring the ability to dynamically change object and event
resolution during run time; those architectural issues are also the subject of
ongoing work. One architectural approach that may resolve some of the
problematic modeling issues just listed is to develop families of models,
rather than single models, at various levels of abstraction (resolution)
(Davis, 1995; NRC 1997; Davis and Bigelow, 1998~. Distributed
simulation systems are being developed to support interoperation of such
model families (Davis, 2001~.
Agent-Based Modeling
Agent-based modeling is a modeling method based on the simulation
of what are called low-level entities, such as individual people or aircraft,
that have simple behaviors but that can produce complex and unexpectedly
realistic collective, or emergent, behavior (Epstein and Axtell, 1996~. As
discussed earlier, such modeling methods are an important area of research
for supporting realistic simulation of complex systems-of-systems (NRC,
1997; Ewen et al., 2000~. A sampling of the open research issues in agent-
based modeling includes achieving satisfactory run-time performance
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RESEARCH AND DEVELOPMENT TOPICS
81
when simulating large numbers of agents, determining an adequate level of
fidelity for individual agents' behavior, validating agent-based models
(Balmann, 2000, Axtel] and Epstein, 1994), and avoiding ad hoc
assumptions during model development (Cederman, 1997~.
Semantic Consistency
Semantic consistency, also known as substantive interoperability,
refers to consistent phenomenological representations of real-world
systems and processes among interacting distributed simulations. For
example, two combat simulations must have consistent models of
intervisibility or they will be unable to interoperate meaningfully in a
distributed simulation (Dahmann et al., 1998~. Research into semantic
consistency and a general mathematical language for expressing models
are recommended (NRC, 1997~.
Dealing with Complexity and Errors
Abstraction is the process of extracting a relatively sparse set of
entities and relationships from a complex reality to produce a valid
simplification of that reality. Abstraction is a general process; it includes
simplification approaches such as aggregation, omission of variables and
interactions, linearization, replacing stochastic processes by deterministic
ones (and conversely), and changing the formalism in which models are
expressed (Zeigler et al., 2000~. The complexity of a model is measured in
terms of the time and space required to execute it as a simulation. The
more detail included in a model, the greater the resources required of the
development team to build it and to execute it as a simulation once it is
built. Validity is preserved through appropriate morphism mappings at
desired levels of specification. Thus, abstraction methods, such as
aggregation, will be framed in terms of their ability to reduce the
complexity of a model while retaining its validity relative to the given
modeling objectives.
inevitable resource constraints require working with models at
various levels of abstraction. As noted above, the complexity of a model
depends on the level of detail, which in turn depends on the size/resolution
product. The size/resolution product reflects the fact that increasing the
size, or number of components, and resolution, or number of states per
component, leads to increasing complexity (Zeigler et al., 2000~. Since
complexity depends on the size/resolution product, complexity can be
reduced by reducing the size of the model or its resolution or both. Given
fixed resources and a model complexity that exceeds these resources, a
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MODELING AND SIMULA TION IN ~NUFAC TURING
trade-off must be made between size and resolution. If some aspects of a
system are represented very accurately, only a few components will be
representable. Alternatively, a comprehensive view of the entire system
can be provided, but only at a low resolution.
Several new approaches to modeling complexity are being developed.
One of them is the notion of coordinated families of simulations at
different levels of resolution, which was mentioned previously. This
approach presupposes the existence of effective ways to develop and
correlate the underlying abstractions.
A second approach, exploratory analysis, attempts to overcome
computational complexity by addressing the issue of optimization, or
searching through large spaces of alternatives for best solutions to a
problem (Davis and Hillestad, 20009. This approach uses low-resolution
models with a wide scope intended to capture the main features of an
overall system or scenario. The approach seeks to exploit the reduction in
the large space of alternatives that low-resolution, or highly abstracted
model structures, may provide.
A third approach fundamentally reconsiders the issue of optimization
as a search for the best among many alternatives. The fast, frugal, and
accurate (FFA) perspective on real- world intelligence provides a
framework for insight into this issue (Gigerenzer and Todd, 1999;
Gigerenzer and Goldstein, 2000~. FFA is taken from the domain of human
decision making in which full optimization is associated with unbounded
rationality. This perspective recognizes that the real world is a threatening
environment in which knowledge is limited, computational resources are
bounded, and little time is available for sophisticated reasoning. Simple
building blocks that steer attention to informative cues, terminate search
processing, and make final decisions can be put together to form classes of
heuristics that perform at least as well as more complex, information-
hungry algorithms. Moreover, such FFA heuristics are more robust when
generalizing to new data, since they require fewer parameters to identify.
They are accurate because they exploit the way that information is
structured in the particular environments in which they operate.
FFAs are a different breed of heuristics. They are not optimization
algorithms that have been modified to run under computational resource
constraints, such as tree searches that are cut short when time or memory
runs out. Typical FFA schemes exploit minimal knowledge, such as object
recognition and other one-reason bases for choice making under time
pressure, elimination models for categorization, and "satisficing" heuristics
for sequential search. In his radical departure from conventional rational-
agent formulations, Simon asserted the bounded rationality hypothesis,
namely, that an agent's behavior is shaped by the structure of its task
environment and its underlying computational abilities (Simon and Newell,
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RESEARCH AND DEVELOPMENT TOPICS
83
1964~. Fast and frugal heuristics are mechanisms that a mind can execute
under limited time and knowledge availability and that could possibly have
arisen through evolution. One illustration of Simon's "satisficing"
alternative to optimization is the "take the first best" inferencing heuristic,
which employs only a fraction of available knowledge and stops
immediately when the first, rather than the best, answer is found. "Take the
first best" does not attempt to integrate all available information into its
decision. It is noncompensatory and nonlinear and can violate transitivity,
the canon of rational choice.
Fundamental Limits of Modeling and Computation
In order to satisfy the needs of M&S for increasingly complex
systems and processes, an integration of the statistics-oriented approach to
M&S research must be emphasized by the academic community and the
computer-science-oriented approach to M&S research must be emphasized
by DOD and industry in acquisition and manufacturing. The statistics-
oriented approach deals with prediction and management of uncertainty,
whereas the computer-science-oriented approach deals with
interoperability, reusability, integration, distributed operation, and
human/machine interfaces. The computer-science-oriented approach is
necessary for the future operational success of defense acquisition and
commercial manufacturing, but as processes and systems become
increasingly complex, estimation and management of uncertainties will
become increasingly important.
Some fundamental limitations in computation in dealing with
complex systems must be recognized. The performance of any future
complex system will be unavoidably stated in probabilistic terms. A suite
of software and a collection of databases may be technically interoperable
and can be used to calculate system performance under a given set of
operating environments, but there is no way that these tools can estimate
the percentage of time that the system will perform satisfactorily under
different circumstances, what the expected performance will be under
uncertainty, or what the confidence level of the estimate is. In order to
answer these questions, Monte Carlo experiments must be run on the
system. Here, one runs up against fundamental limitations of performance
simulation involving uncertainties.
"There are fundamental limitations to improve the
simulation speed due to fact that confidence interval of
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Representative terms from entire chapter:
defense acquisition
84
MODELING AND SIMULA TION IN MANUFACTURING
performance estimate decreases at best at the rate of
1/n~/2 where n denotes the length of simulation."
This is a heavy computational burden that may become too much for
complex systems. In addition, in order to improve the system performance
estimate by adjusting or tuning various parameters in different phases of
the acquisition process, dimensionality, or combinatorial explosion, must
be dealt with. The search space of system design parameters is
combinatorially huge.
The first fundamental limitation in computation states that each
system performance evaluation via simulation is time consuming. The
second limitation states that a very large number of such evaluations may
be necessary. These difficulties are multiplicative. Finally, there is a third
limitation.
"No Free Lunch Theorem": Without specific structural
assumptions, there exists no optimization or search algorithm
that can perform better on the average than blind search in
dealing with the first and second limitation. (Ho, 1999, p. 8)
These three limitations are fundamental limits on computation in
dealing with complex systems. No amount of theoretical, hardware, or
software advances can overcome them. Consequently, a strategic
redirection is called for in dealing with them. Several emerging trends that
directly or indirectly address the problem of system engineering of
complex systems are outlined below. One or more of these topics may
blossom into proven tools for dealing with the preceding difficulties and
enable a more quantitative and optimizing approach.
Ordinal Versus Cardinal Optimization
Order is much easier to ascertain than value is. If one holds two
identical-looking boxes in either hand, it is easy to determine which one is
heavier, but much harder to determine how much heavier one is than the
other. In many complex decision problems, it is often sufficient to be able
to determine which solution is better, or to determine which is in the top 1
percent, rather than which is the absolute best. A theory of ordinal
optimization is being developed that may enable quantitative
measurements of such assertions via simulation modeling without having
' Y.C. Ho, Ordinal Optimization Teaching Module. Available at
RESEARCH AND DEVELOPMENT TOPICS
85
to confront the first and second fundamental limitations on computation of
complex systems (NRC, 1999b).
Off cient Search Via Learning
Blind search in a large space is inefficient. Therefore, to deal with
the large search spaces imposed by the second and third computational
limitations discussed above, the structure of specific problems must be
learned along the way. A number of automated learning theories currently
in vogue in artificial intelligence research, such as knowledge discovery,
data mining, Bayesian networks, and Tabu search, may be significant for
developing M&S capabilities. Tabu search is a heuristic technique for
search in combinatorial optimization problems (Grover, 1990~.
Errors in Distributed Simulations
Given fixed resources and a model complexity that exceeds these
resources, a trade-off must be made between size and resolution. If some
aspects of a system are represented very accurately, only a few components
will be representable. Alternatively, a comprehensive view of the entire
system can be provided, but only at a low resolution. Such resolution may
introduce errors that may pose particular problems in distributed
simulations. In such complex, networked systems of models, owing to low
resolution each model will typically be in error to some degree. Therefore,
it is natural to expect that in a complex system of many linked models,
even if individual inaccuracies are small, such errors can accumulate,
propagate, and reinforce each other, rendering the behavior of the
aggregate significantly different from the behavior of the real system.
Error propagation in distributed simulations plays an important role in
verification, validation, and accreditation, and therefore is an important
area of research that needs to be strengthened. In the current state of the
art, it is possible to suggest that such error propagation may, or may not be,
a significant issue in distributed simulations. On the one hand, modeling
errors in complex systems can be like noises that are more or less
statistically independent. The cumulative effect of many independent errors
behave according to the central limit theorem and decrease with increasing
complexity under some reasonable assumptions. A simple case is the law
of large numbers, which improves accuracy by averaging many
measurements. A second mitigating factor is the theory of ordinal
optimization, mentioned above. Research here has shown that for the
purpose of comparison (i.e., which is better?), very crude models are quite
sufficient. Consider the metaphor of two bags of gold. You are free to
choose the heavier bag. Every one of us can unfailingly tell the heavier
86
MODELING AND SIMULATION IN MANUFACTURING
bag, even with small differences. But most of us will have difficulty if we
are asked to estimate accurately the difference in weight between the two
bags. "Value" is much harder to estimate than "order." In most cases of
simulation optimization, we only need to know the order or be able to
locate the top 1 percent of the design. It is not necessary to know the
performance "value" accurately. Approximate simulation models are quite
adequate for the former purpose. Once the top 1 percent have been located
with high probability, we can lavish our attention and computing budget on
this much smaller subset. A large volume of literature on the theory and
success stories has been built up on this subject during the past decade (Ho
and Cassandras, 2001~.
On the other hand, it is known from work on numerical analysis, that
numerical methods can introduce instabilities that greatly magnify errors
even if the underlying models are stable. To obviate error-induced
instabilities, criteria that enable choice of time-step size and other
controllable factors are well known for nondistributed simulations.
However, the major difference between distributed simulations and their
nondistributed counterparts is that control and data are encoded in time-
stamped messages that travel from one computer to another over a
(bandwidth limited) network (Fujimoto, 2000a). Traditional analyses in the
design of numerical methods consider trade-offs between accuracy and
speed of computation (Isaacson and Keller, 1966~. However, since
distributed messaging requires that continuous quantities be coded into
discrete packets and sent discontinuously, it is more appropriate to
consider discrete event simulation as a natural means to consider accuracy
or bandwidth trade-offs. Recent work has shown that significant
reductions of message bandwidth demands (number and size of messages)
with controllable error and local computation costs are possible2 (Zeigler et
al., 1999~. Finally, the issue of numerical stability in complex simulation is
related to the problem of sample path continuity with respect to parameter
and timing perturbation. Here again, literature exists (Ho and Cassandras,
1 997~.
Theory of Complex Systems
Complex systems, such as the national electric power-grid and
worldwide communications networks, are vulnerable to attacks and
catastrophic failures (Amin, 2000~. A theory of complex systems is
emerging that may shed light on the fundamental nature of such complex
interconnected systems, why and how they fail, and the limits to and
2 The interested reader may wish to consult Chapters 14 and 16 in Zeigler et al. (2000) for an
extended discussion of error in modeling and distributed simulation.
RESEARCHAND DEVELOPMENT TOPICS
87
disadvantages of complexity (Ho and Pepyne, 2001). This is related to the
problem of inferring total system performance from that of components.
Any system is assembled or constructed from a set of components and/or
suboperations. When broken down to the elemental constituent part, each
part or suboperation can be modeled, and its performance measured, even
if probabilistically. However, each part's contribution to the overall
performance success or failure—of the entire system is different. For
example, in an unmanned combat air vehicle, the performance of the
automatic target detection subsystem is more important than is the
successful landing of the returning system. The former directly affects the
success of the mission, while the latter may cause the destruction of an
expendable system. There is need for an analysis technique for assessing
the relative expected importance and contribution of each part or
suboperation to the overall goal of a system engineering project as a
function of network architecture and hierarchy. Such a tool would enable
managers to measure the critical elements of a systems engineering project
and direct resources at those parts more systematically and quantitatively.
Uncertainty
Uncertainty is becoming increasingly important in modeling and
simulation. Characterization of uncertainty refers to methods for tracking
and quantifying the propagation through a model's calculations of the
uncertainty that is inevitably present in the attribute values and interactions
of components within a simulation. Decision making under uncertainty
refers to models that assist in evaluating uncertainty and risk in situations
in which incomplete information is available. Exploratory analysis under
uncertainty is a process of searching the space of possible simulation
outcomes as a function of the many assumptions in a scenario in order to
find and delimit interesting or dangerous outcome regimes (NRC,
1 997a,b).
MODEL INTEGRATION
The infrastructure for modeling consists of fools and capabilities that
support the practice of modeling. This infrastructure must support model
integration and interoperability in order for the M&S requirements of
acquisition and manufacturing to be met. Important topics associated with
model integration are interoperability, composability, integrating
heterogeneous processes, and linking engineering with effectiveness
simulations.
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MODELING AND SIMULATIONIN MANUFACTURING
Moorby, 1991~. It is easier to learn, but lacks constructs to support system-
leve] design.
Unfortunately, VHDL is limited in the representation of mixed
analog and digital processing. A framework for the modeling and
simulation of hybrid analog/digital systems has long been needed. Today,
the need to design mixed-signal chips (MSCs) to support the growth in
wireless devices and next-generation automotive electronics has brought
this problem to the foreground. MSCs have been implemented as custom-
application-specific integrated circuits (ASlCs), but must now be mass-
produced for use in wireless technology. MSCs receive analog signals,
process and manipulate them mainly in digital form, and reconvert them
back to analog form. The challenge for systems design is the high level of
functionality of an MSC. It contains radio frequency components, such as
receivers, antennas, filters, and amplifiers; analog components, such as
digital-to-analog converters, battery and power supplies, and interfaces to
sensors; and digital components, such as digital signal processors,
microcontrollers, microprocessor memory, analog-to-digital converters,
and interface buses.
Hybrid design has traditionally been tackled by mapping the input-
output behavior through thresholding and interpolation. The fundamental
difficulty is that, driven by the needs of accuracy and efficiency, the
resolutions of time in the respective simulations for the analog and discrete
subcomponents may be different. This translates into different units of
time. While techniques such as lock-step, fixed time step, ping-pony, and
Calaveras have been proposed in the literature, they are essentially
arbitrary and lack a scientific basis to yield a common notion of time. The
difficulty is aggravated when analog and discrete subsystems occur in
feedback loops. Current efforts to solve this problem merely extend the
previous methodology by standardizing the input-output signals for
exchange between the simulations of analog and discrete subsystems. New
approaches are needed (Ghosh and Grambasi, 20011.
Linking Engineering and Effectiveness Simulations
It is useful to distinguish between two broad classes of simulations.
The first is product modeling or engineering simulations, which simulate
the physics of products or systems being designed with a high degree of
detail and physical fidelity. The intent of these simulations is to assist
design engineers in understanding the physical performance of the product
or system as designed. They often simulate only one system or subsystem
at a time and run slower than read time. They can be loosely defined as
using M&S to determine how to build a system. The second class of
simulations is performance modeling or effectiveness simulations, which
RESEARCH AND DEVELOPMENT TOPICS
simulate products or systems that are assumed to exist and operate as
designed. The intent of these simulations is to determine how effective the
systems would be in use, or what performance parameters the systems
must have in order to be effective in use. They often simulate scenarios
involving many simulated systems and run in real time or faster. They can
be loosely defined as using M&S to determine which system to build. The
ability to link these two types of simulations is necessary for achieving the
goals of defense acquisition. The ability to reuse engineering models and
simulations in effectiveness simulations would save time and money.
93
MODEL CORRECTNESS
Model correctness is the fundamental requirement of ensuring that the
predictions of a simulation model can be relied upon (Zeigler, 1998~. The
vision of defense acquisition contained in SBA requires the development
of accurate and reliable models of real-world systems. A prerequisite to
this is an understanding of the real-world systems and objects to be
modeled, their contextual domains, and the phenomenology of their
operations and interactions, all at a level of detail sufficient to justify the
model. Once the models have been implemented as simulations, their
correctness must be rigorously evaluated.
Domain Knowledge
Improved understanding of the real-world basis for models is needed
in the areas of phenomenology of warfare, physics-based modeling, and
human behavior modeling.
Phenomenology of Warfare
The military domain is of special importance because it is the primary
focus of SBA and because it is the domain in which human lives are most
likely to be risked on the basis of decisions made using M&S. Lack of
recent investment is not compensated for by previous investment because
of the rapidly changing nature of military technology, doctrine, and
operations. For example, models are lacking in such emerging areas as
information operations and operations other than war. Effort is needed to
develop deeper, more rigorous, and more quantitative understanding of the
phenomenology of warfare, especially involving the complex,
interconnected, and nonlinear military systems and systems-of-systems
94
MODELING AND SIMULA TION IN MANUFACTURING
planned for the future. Relatively little recent investment has been made in
understanding the phenomenology of military operations at the mission
and operational levels (NRC, 1997a,b).
Physics-based Modeling
Mathematical models in which the equations that constitute the model
are those used in physics to describe or define the physical phenomenon
being modeled are referred to as physics-based models. For example,
physics-based flight dynamics models use aerodynamics equations rather
than look-up tables to model the flight characteristics of a simulated
aircraft. The physics of failure and assessment of a potential system's
durability and operational availability is of special interest. Such
assessments would greatly benefit from accurate physical models that
support predictions of the modes and times of failure of physical systems.
Several studies have concluded the need for improvements in physics-
based modeling (Johnson et al., 1998; Hollis and Patenaude, 1999; Starr,
1998~. Physics-based modeling is arguably more important for defense
manufacturing and acquisition than for other simulation applications such
as training.
Human Behavior Modeling
Computer-generated forces are often used in training simulations to
provide both opposing forces and supplemental friendly forces for human
participants in a simulation. They are also often used to generate all of the
entities in battlefield simulations being used for nontraining purposes, such
as analysis, experimentation, and SEA. Automated or semiautomated
entities are created, and their behavior is controlled by the computer
system, perhaps assisted by a human operator, rather than by human
participants in a simulator (Kerr et al., 1997; Petty, 1995~. These
automated behaviors are produced by algorithms based on models of
human behavior. The reliability of the results depends on the validity of the
behavior-generation methods. While current behavior-generation methods
are reasonably effective at producing behavior that is in accordance with
straightforward tactical doctrine, they fall far short of producing
realistically human behavior with all its unpredictability and sophistication.
Several studies have concluded that a need exists for improvement in
human behavior modeling (Ewen et al., 2000; NRC, 1998b; Hoagland et
al., 2000; Starr, 1998; Johnson et al., 19981.
RESEARCHAND DEVELOPMENT TOPICS
Verification, Validation, and Accreditation
95
Verification is the process of determining that a model
implementation, or simulation, accurately represents the developers'
conceptual description and specifications. Validation is the process of
determining the degree to which a model and associated data are an
accurate representation of the real world, with respect to the model's
intended use. Accreditation is the process of official certification that a
model or simulation is acceptable for use for a specific purpose. Several
studies have identified verification, validation, and accreditation as
important topics for research and development (Johnson et al., 1998; Ewen
et al., 2000; Hollenbach, 2000; SBATF, 1998~.
A crucial step in the acquisition of a defense system is operational
testing and evaluation, the final assessment of a system's effectiveness and
suitability prior to fielding. Traditionally done using real-world testing of
actua] systems, operational testing has seen a gradual increase in the use of
M&S to reduce time and costs.
This application of M&S requires extremely accurate simulations and
consequently requires highly reliable validation methods. As M&S is used
more in operational testing, the demands on the validation of the
simulation will increase. Severa] advances in statistical methods are
relevant to validation of simulations used for defense acquisition and may
provide the basis for needed improvements in validation methods (NRC,
1998a). The limits of applicability of M&S to operational testing have been
clearly asserted by the commanders of the services' operational testing
organizations (Besa] et al., 2001~. Results generated by models and
simulations used may be the basis of decisions affecting human safety or
expending large sums of money. Validation methods that quantify the
bounds of validity and risk of error in a mode] can help to establish the
limits of M&S app]icabi]ity in operational test and evaluation.
STANDARDS
Standards are at the intersection of technical and nontechnical issues.
The ways in which standards are developed are complex and often more
successful if done from the ground up rather than from the top down. The
M&S community has historically been resistant to setting standards.
Because many M&S practitioners are self-taught or have had largely on-
thejob training, there are many different methods of doing things. The
variety of modeling methods is commensurate with the range of systems
modeled.
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MODELING AND SIMULA TION IN A1ANUFAC TURING
Currently, a state-of-the-art, standardized external model
representation is lacking. Moreover, modeling languages do not adequately
support the structuring of large, complex models and the process of model
evolution in general. The development and application of standards,
however, are essential to the achievement of the level of interoperability,
integration, and reuse envisioned for commercial manufacturing and
defense acquisition. This section discusses existing modeling and
simulation standards, general software standards, and higher-layer
standards, and needs for their development and integration.
Modeling and Simulation Standards
Limited interoperability exists among the modeling and simulation
environments available today. However, several standards are emerging
that are aimed at solving interoperability and model construction problems.
High Level Architecture
.
HLA is a general-purpose architecture for simulation reuse and
interoperability. It was developed under the leadership of the Defense
Modeling and Simulation Offices (DMSO) for the purpose of supporting
reuse and interoperability across the many different types of simulations
developed and maintained by DOD. In 1996, HLA was approved as the
standard technical architecture for all DOD simulations, and in 2000, it
was approved as an open standard by the Institute for Electrical and
Electronic Engineers (IEEE). DMSO sponsored the establishment of the
Simulation Interoperability Standards Organization (SISO) as the
organization responsible for the promulgation of applications of the HLA
standard. Currently, HLA addresses technical interoperability, or the
standardization of data interchange among model components at run time.
However, it does not address substantive interoperability, the ability to
assure that data have common meanings among components so that a
coherent federation emerges capable of meeting the objectives of its
designers. This capability should be developed.
Modelica
An early attempt at M&S standardization, the Continuous System
Simulation Language (CSSL) was first published in 1967. CSSL defined
requirements for a standard continuous simulation modeling language, but
had limited impact. Mode]ica is the current manifestation of continuous
system modeling standardization efforts (Elmqvist, l 9991. The Modelica
RESEARCHAND DEVELOPMENT TOPICS
97
Association, a nonprofit, nongovernmental association consisting of the
members of the original Modelica Design Group, was established in 2000
to promote the development and application of the Modelica computer
language for modeling, simulation, and programming of physical and
technical systems and processes.
The Modelica effort is based on recent research results. Object-
oriented modeling languages have already demonstrated how object-
oriented concepts can be successfully used to support hierarchical
structuring, reuse, and evolution of large and complex models independent
of the application domain. Noncausal modeling demonstrated that the
traditional simulation abstraction can be generalized by relaxing the
causality constraints, or by not committing ports to an input or output role
too early. These results have the potential for enabling both simpler models
and more efficient simulation.
Discrete Event System Specif cation
DEVSis a formal modeling and simulation framework based on
generic dynamic systems concepts (Zeigler et al., 2000~.DEVS contains
well-defined concepts for the coupling of components; hierarchical,
modular model construction; supporting discrete event approximation of
continuous systems; and supporting repository reuse with an object-
oriented substrate. DEVS contains important abstract concepts
underpinning the representation of mixed-signal electronic designs. The
concepts of system modularity and component coupling to form composite
models are well defined. The closure under coupling property allows
coupled models to be treated as components and therefore supports
hierarchical model composition. Advantages of the DEVS methodology
for model development include well-defined separation of concerns
supporting distinct modeling and simulation layers that can be
independently verified and reused in later combinations with minimal
reverification. The resulting divide-and-conquer approach can greatly
simplify and accelerate model development, leading to greater model
credibility with less effort.
The DEVS methodology has been realized in high-level languages
such as C++ and Java and has been extended for parallel and distributed
execution. For example, DEVS-C++ models have been executed on
parallel machines. Implementation of DEVS-C++ over message-passing
interfaces can afford parallel execution of models and thus supports
efficient, high-performance simulation of large-scale models. Furthermore,
DEVS-C++is the basis for DEVS/CORBA, a distributed modeling and
simulation environment formed by mapping the DEVS-C++ system onto
the common object request broker architecture (CORBA) middleware.
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MODELING AND SIMULA TION IN MANUFACTURING
Models developed in DEVS-C++ or DEVS-JAVA can be directly
simulated in parallel and/or distributed environments over any transmission
control protocol/internet protocol (TCP/IP), asynchronous transfer mode
(ATM), or other network. The DEVS formalism is both a universal and
unique representation of discrete event dynamic systems. It has been
combined with the differential equation formalism to form a composite
formalism with a well-def~ned semantics that is able to express hybrid
digital/analog systems.
General Software Standards
M&S standards fall outside the category of general software
standards, because the body of knowledge intrinsic to M&S generates
additional requirements that are left open in more general standards.
However, it is worth reviewing the state of M&S-related software
standards, as M&S standards must eventually mesh with them.
Unit ed Modeling Language
Software engineering promotes systematic, disciplined, and
quantifiable approaches to the development, operation, and maintenance of
software-intensive systems. By applying engineering principles to
software, it strives to bring together methods, processes, and tools in a
unified fashion. While fundamentally different approaches to software
engineering have emerged in recent years, the object-oriented approach has
become widely accepted and practiced (Booch, 1994, 1997; Pressman,
1996; UML, 2000~. In the object-oriented worldview, the software
development process includes conceptualization, analysis, design, and
evolution (Booch, 1997) and supports the architecture-driven paradigm
based on a hybrid of spiral and concurrent software development
processes. Adherents of the object-oriented approach consider it superior to
other software development approaches such as functional and procedural.
Furthermore, the modular architecture-driven approach can strongly
support incremental, stepwise, iterative specification, design, and
development of hardware and software components concurrently. Other
advantages of the object-oriented approach include support for scalable
high-performance execution and model development; dynamic
reconfiguration; systematic and incremental verification and testing; and
team-oriented development. The adaptation of object orientation to
software engineering has become increasingly indispensable for systems
exhibiting heterogeneity and demanding flexibility in terms of both
software and interoperability with multiple hardware components.
RESEARCH AND DEVELOPMENT TOPICS
99
The unified modeling language (UML) has been managed by the
vendor-neutral Object Management Group (OMG) since 1997. UML
originated as a combination of approaches to software modeling developed
by James Rumbaugh, Ivar Jacobson, and Grady Booch, but has now
evolved into a public standard. OMG committees are defining ways in
which the next version of UML can facilitate activities such as the design
of Web applications, enterprise application integration, real-time systems,
and distributed platforms. UML attempts to support a higher-level view of
design and coding in terms of diagramming. However, the majority of
developers still build in source code, working with linguistic rather than
spatial intelligence. UML vendors are attempting to educate programmers
to pay attention to design views, allowing users to decide which design
view they want to see at any given time. UML definition is still in a state
of flux. For example, many proponents believe that its features should be
reduced to a small core, or kernel. One proposal for such a kernel would
include use cases, class diagrams, and interaction diagrams but would
exclude state charts and activity graphs that provide some of the richest
semantics in UML.
UML is aimed at general software development, primarily for
business applications, and is not simulation-aware. UML is the union of at
least 10 techniques for diagramming notation. However, there is much
more to consider than diagramming in the realm of software engineering,
and in particular, software development for models and simulations. In
addition to the factors relating to all software, which include software
design principles, exploiting patterns, and scalable architecture, the M&S
developer must understand the particular characteristics of dynamic
systems, the error properties of numerical algorithms, and the intricacies of
parallel and distributed simulation protocols. Although state diagrams are
included in UML, they are not adequate to handle the variety of dynamic
systems of interest in M&S. UML does not support model construction
from dynamic system components or from reusable model components as
required for SBA. Fundamentally, UML should be applied to the
development of software to support modeling and simulation, but not to the
construction of dynamic system models.
Common Object Request Broker Architecture
Middleware technology evolved during the 1 990s to provide
interoperability in support of the move to client/server architectures. The
most widely publicized middleware initiatives are OMG's CORBA,
Microsoft's distributed component object model (DCOM), and DOD's
HLA run-time infrastructure (RTI) (Dahmann et al., 1998~. Middleware
simplifies the integration of heterogeneous systems so that users can share
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MODELING AND SIMULATIONIN MANUFACTURING
infonnation more efficiently, more cost-effectively, more flexibly, and
more extensively. It will become more critical as the Web matures and
systems become even more distributed.
Middleware services are sets of distributed software that exist
between the application and the operating system and network services on
a system node in the network. Middleware services provide a more
functional set of application programming interfaces (APIs) than the
operating system and network services in order to allow an application to
locate transparently across the network, be independent from network
services, be reliable and available, and scale up in capacity without losing
function.
The ability to operate in real time imposes additional stringent
requirements on services that are not part of the middleware standard.
Operating in real time implies not necessarily speed, but consistency or
predictability of response as measured by small jitter, for example. Real-
time object-oriented middleware attempts to provide parameterized objects
that can be composed to provide quality of service guarantees to
application-layer software. The ACE ORB (TAO), which is an extension
of CORBA, is being developed to demonstrate the feasibility of using
CORBA for real-time applications versus direct socket-level programming
(Schmidt et al., 1998~. Real-time middleware being developed includes
real-time extensions to message-passing interfaces (MPl/RT's) (Kanevsky
et al., 1997) and real-time dependable (RTD) channel. The latter is based
on CactusRT (Hiltunen et al., l 999), which was developed at the
University of Arizona in an effort to make communication services with
enhanced quality of service (QOS) guarantees related to dependability and
real time in the context of distributed real-time computing. ARMADA is
another set of communication and middleware services that provides
support for fault-tolerance and end-to-end guarantees for embedded real-
time distributed applications (Abdel Zaher et al., 19991.
Higher-Layer Standards
M&S is an enabling technology to the larger activities encompassed
by systems engineering. Standards are emerging within this larger context
as well, and it is important that these standards develop in a manner
compatible with M&S.
Generalized Enterprise Reference Architecture and Methodology
As indicated earlier, GERAM is a developing standard for enterprise
engineering, which is broadly concerned with designing and redesigning
RESEARCH AND DEVELOPMENT TOPICS
101
systems for industrial, administrative, and service applications (Vernadat,
1996~.
Different modeling methods to support enterprise engineering have
been proposed for different applications, including integrated computer-
aided manufacturing definition methods for functional modeling, entity-
relationship techniques for information systems, object-oriented
approaches, decision system analysis, and activity-based costing methods
for economic evaluation. However, few integrated methods exist to cover
all of the aspects of a business entity. CIMOSA provides full coverage of
four fundamental aspects of enterprise modeling - 1) function, (2)
information, (3) resource, and (4) organization and clearly differentiates
and represents the three fundamental types of flow in any enterprise: (1)
materials, (2) information/decision, and (3) control flows (Vernadat, 1998~.
However, current modeling and simulation tools are unable to support
these modeling concepts fully, and standards are needed for such tools to
support the growing use of the GERAM methodology.
Data Exchange Standards
Indust~y-based organizations have undertaken the development of
several standards for data exchange that relate to and can advance the
interoperability of models and simulations. The family of standards
developed by the international Organization for Standardization known as
the Standard for the Exchange of Product Model Data (STEP) aids in the
exchange of computer-aided design (CAD), computer-aided manufacturing
(CAM), and other types of product data. However, this family of standards
has been over a decade in development, and there remains some resistance
to its adoption in some commercial tools.
During the last several years, significant progress has been made on
the XML3 for data exchange. XML is applicable to the exchange of
virtually any type of data, and a number of business and technical
communities have developed associated standards using nomenclature
common in those individual communities.
CONCLUSIONS
The complexity of planned and existing systems-of-systems is
growing more rapidly than the power of the computational and modeling
methodologies needed to simulate them. For example, multiresolution
3 Further information is available at .
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MODELING AND SIMULA TION IN MANUFACTURING
models that can reliably predict the effect of system design changes on the
output of systems-of-systems operations do not exist. Achieving the
comprehensive SBA vision requires an understanding of the fundamental
limitations associated with the simulation and modeling of complex
systems that does not currently exist. Those limitations cannot be
overcome without advances in hardware and software and may require
basic reformulation of the SBA problem. Research is needed to determine
the theoretical and practice] limits of modeling and computation with
respect to manufacturing and acquisition and to devise methods to work
within and around those limits. To support the envisioned use of M&S,
research is needed in modeling theory, especially multiresolution/
multiviewpoint modeling, agent-based modeling, and semantic
consistency; and in modeling methodologies for dealing with uncertainty.
Advances in technology, such as parallel computing, distributed
computing, and distributed simulation, have begun to make integration and
interoperability of simulation systems practical. However, the breadth of
the comprehensive SBA vision, including model integration across all of
the SBA viewpoints, is beyond current hardware and software capabilities.
Research is needed to expand current model integration and interoperation,
including that between engineering and effectiveness simulations. Setting
standards for simulation interfaces and interoperability for system design
data, including file formats or format descriptors, is timely and appropriate,
and will allow improved interoperability and reuse. Standardization of
tools may not be appropriate at this time.
In order to ensure correctness of the models in use, research is needed
in domain knowledge at a level of detail that can serve as the basis for
models in domains relevant to manufacturing and acquisition. Research is
needed in verification, validation, and accreditation, especially validation;
and in human-behavior modeling, including modeling of cognition and
belief. Finally, standards for interfaces and operability must be developed
and applied to modeling and simulation software, general software, and the
frameworks being developed for integrating other software systems.
