Simulation-Based Engineering of Complex Systems

Speaker

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Dr. John R Clymer
Dr. Carol Jacoby

Location

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California State University Fullerton

Abstract

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Study of a large number of complex systems during the last 40 years by Dr. Clymer and others, including computer, transportation, manufacturing, business, and military systems, has shown that complex systems are best characterized as a set of interacting, concurrent processes. This discovery inspired the development of Context-Sensitive Systems (CSS) theory, based on mathematical linguistics and automata theory, as a way of thinking about complex systems using interacting concurrent processes. During the 1968-1971 time frame, Dr. Clymer developed a graphical modeling language, Operational Evaluation Modeling (OpEM), to express CSS models of both existing and conceptual systems. During the same time period, an alternative approach, Petri nets, was developed independently of OpEM. Subsequently, after 20 years of using procedureoriented simulation programs to design and evaluate complex systems, a graphical, object-oriented, discrete-event simulation library, OpEMCSS, was developed that works with ExtendSim to enable rapid development of CSS models and simulations in the OpEM language.

Since an OpEMCSS simulation is an abstract description of a complex system, understanding how the simulation works assists the systems engineer in understanding how the complex system works, allowing the system design to be optimized to meet stakeholder requirements. In this tutorial, it is shown thatCSS theory, OpEM modeling language, and OpEMCSS library can be applied to understand Complex Adaptive Systems and to perform Model-Based Systems Engineering (MBSE).

MBSE mitigates system development problems that are caused by the failure to optimize the interoperability and synergisms among all component algorithms and methods at the overall system level. Further, the interactions of the system with its external systems and the dynamic demands of the operational environment on the system must be included in a MBSE systemlevel model and evaluated for tradeoffs.

An OpEMCSS system model provides the structure andontology needed to connect detailed component models for MBSE. The MBSE approach presented in this tutorial is:

1. Apply the OpEM top-down systems design methodology.
2. Perform system-concept and top-level design tradeoffs to optimize stakeholder requirements using OpEMCSS
3. Produce a systems design specification that includes component interface and qualification requirements using a design-capture database tool.
4. Develop detailed models of alternative component algorithms and methods using OpEMCSS special blocks.
5. Perform virtual systems integration and system validation and verification using the system-level OpEMCSS simulation.
6. Determine impact of requirements changes and conduct detailed design trades using the system-level simulation.

OpEMCSS graphical simulation library works with the commercial software tool ExtendSim, which was chosen for two major reasons. First of all, ExtendSim is relatively inexpensive for people to buy and use. The OpEMCSS icon-blocks automatically provide more than 95% of all simulation code that in the past had to be programmed by hand. In context-sensitive systems, these programming details are very complex and would otherwise require extensive programming skill and effort to accomplish. ExtendSim, with the OpEMCSS library, gives systems practitioners the ability to experiment with complex, contextsensitive interactions and quickly build a model. Time is not wasted dealing with complex programming details and writing extensive code, but rather the emphasis is on complex systems design, analysis, and evaluation for MBSE.

All CAS have emergent behaviors that result due to the interactions of their components. There are three kinds of interactions discussed in this tutorial, but one interaction, communication and adaptation, leads to emergent behavior in a CAS. Such behaviors occur only if components are working together; they do not occur when operating any single component alone. Thus, we cannot understand each component as it operates independently to gain an understanding of the whole system. Often the emergent behavior of the system is not predicted when a system concept is proposed, and its occurrence is a surprise when the system concept is built. This is why simulation of the entire system is an important part of MBSE.

As an example of a system having emergent behavior, a distributed vehicle traffic control network located in a large city is discussed in this tutorial. This traffic control network is an example of a System of Systems (SOS), where each system in the network independently provides specific services and each system can operate independently of the rest of the SOS. Additional services are provided through collaboration among the networked systems. Network-Centric Operation (NCO) of related business units and combat system platforms are other examples of SOS that are currently of research interest.

Each major intersection has a vehicle traffic light controller to determine traffic light timing. In this system, each traffic light controller uses its perceptions about incoming traffic flow to optimize light timing, thus minimizing local vehicle waiting time. The result of each traffic light controller adapting light timing to accommodate traffic flow coming from other intersections is to minimize the average waiting time in the entire network. Global minimization of traffic waiting timeresults as a consequence of the emergent behavior of this system, which is the self-synchronization of each traffic controller's light timing with other controllers. As light timing control in the overall traffic grid evolves, a complex but definite pattern in network operation, north-south, red-to-green transition times, emerges out of an initial random light pattern. The emergent behavior of the traffic grid cannot be explained through an understanding of each controller alone. Understanding only comes when we study the interactions of the controllers as they adapt their behaviors in response to perceived information about incoming traffic flow, achieving selfsynchronization of all traffic light controllers in the network.

ExtendSim+OpEMCSS can be used in any field that is concerned with entities that perform a set of tasks that lead to satisfaction of a measurable goal that may or may not be explicitly known or stated. Such fields include project management, systems engineering, software engineering, industrial engineering, business organizations, societal systems and sociology, biological and ecological systems, economic systems, and others. Thus, this tutorial is designed for a broad spectrum of people who wish to gain an understanding of complex systems and MBSE. It will be shown that, although complex systems have behaviors that are often difficult to understand, the underlying ExtendSim+OpEMCSS modeling building blocks comprising a complex system model are simple and easy to understand.

Biography

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Dr. John R. Clymer obtained his Doctor's Degree in Electrical Engineering from Arizona State University in 1971. He currently is a Professor of Electrical Engineering at California State University, Fullerton (CSUF). He consults on a regular basis in the area of systems engineering (mission analysis and conceptual systems design), simulation, and artificial intelligence. In addition to consulting, he has held numerous lectures and has presented technical courses throughout the United States and abroad. His teaching assignments have included Computer Engineering, System Control, Continuous Systems Simulation, Operational Analysis and DES simulation, Optimization and Mathematical Programming, and Artificial Intelligence (fuzzy logic and control, neural networks, and expert systems). Dr. Clymer's current research is focused in the area of intelligent-systems design, including multi-agent systems (SOS and NCO), and Model-Based Systems Engineering methods, applying integrated simulation, artificial intelligence,  einforcement learning, and evolutionary programming methods to advance the state of technology in those methods and the use of SOS and NCO. He is a founding member of the Applied Research Center for Systems Science at CSUF. He is a member in good standing of IEEE and INCOSE.

Dr. Carol C. Jacoby brings 28 years of experience as a systems engineer and manager in the aerospace and defense industry to her teaching. She was the manager of the Hughes Mission Analysis Center of Excellence, and System Architect for both defense and transportation programs. Dr. Jacoby has built a reputation as an expert in developing complex information-intensive systems. Currently, she is the founder and of Jacoby Consulting, specializing in front-end systems engineering and decision analysis. Dr. Jacoby has taught systems engineering courses throughout the country. She was one of the first to apply systems engineering techniques to highway transportation systems during the early days of Intelligent Transportation Systems (ITS). She co-authored the Systems Engineering Guidebook for ITS for Caltrans and the Federal Highway Administration. She is the author of numerous technical papers and the book, Simple Spreadsheets for Hard Decisions, which teaches planning for the future by modeling likely outcomes.