Discrete event simulation of object movement and interactions

Corey, Philip D.; Clymer, John R.

doi:10.1177/003754979105600305

Cited by 18 publications

(4 citation statements)

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“…The Operational Evaluation Modeling (OpEM) graphical discrete event simulation language [12,13] was developed to describe the context-sensitive transitions required for CAS interactions. The challenge in developing the graphical version of this language was to apply lessons learned from experience obtained from the OpEM Pascal Simulation programming system [2,[12][13][14][15][16][17][18][19] used during the past 15 years. This system, though flexible in terms of data structures and event action code that can be created, is not easy to program.…”

Section: Discussionmentioning

confidence: 99%

Simulation-Based Engineering Of Complex Adaptive Systems

Clymer

1999

SIMULATION

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Systems to be developed in the future will likely be more complex than in the past. The number of behaviors a system can exhibit is one measure of system complexity. A complex system can be described as a large network of communicating subsystems. A regional or national Intelligent Transportation System exemplifies such a system. If each subsystem in the network adapts its behavior to collaborate with other subsystems to achieve overall goals, such a system is called a Complex Adaptive System (CAS). In this paper a building elevator system provides an example CAS in which each elevator collaborates with others to optimize system performance. A Context-Sensitive System model that explicitly deals with the complexity of CAS is dicussed. A graphical simulation language, Operational Evaluation Modeling for Context-Sensitive Systems (OpEMCSS), assists in understanding CAS and automates most complex simulation details and semantics. OpEMCSS is distinctive from most simulation languages that have a graphical user interface in its ability to duplicate processes without having to show every instance onscreen. OpEMCSS and its use in simulation-based systems engineering of CAS are discussed.

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Section: Discussionmentioning

confidence: 99%

Simulation-Based Engineering Of Complex Adaptive Systems

Clymer

1999

SIMULATION

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“…The second process includes enter-leave-sonar-feature-region events that define when a ship signal is detectable by a sonar subsystem. The moving object routines discussed in [15] simulate these relationships simply and efficiently. These enterleave range events trigger the decision making processes so that the continuous motion of the ships need not be modeled.…”

Section: Induction Of Sonar Classification Rulesmentioning

confidence: 99%

“…An OpEM [7][8][9][10][11][12][13][14][15] simulation program was developed that simulates surface ships and submarines transiting a surveillance area. A collection of signals was associated with ship type, either surface ship or submarine, each having a particular speed.…”

Section: Introductionmentioning

confidence: 99%

Induction Of Classification Rules From Noisy Sonar Features

1994

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Developing rules to classify surface ships and submarines based on noisy features extracted from underwater acoustic signals is difficult and time consuming; indeed, it is difficult to generate decision making rules for any complex system. The Operational Evaluation Modeling (OpEM) Simulation Tool Kit including Expert System Controller and Induction Program, discussed in this paper, greatly mitigates this problem for most complex system simulation projects such as airports, factories, or space defense systems. A theory for inductive learning of decision making rules in cybernetic systems is presented to provide a concise way-of thinking about inductive learning. An OpEM directed graph model is presented that describes operation of a sonar detection and classification system. An OpEM Pascal simulation of this system demonstrates how effective classification rules can be that are induced from simulation generated cases, even with noisy features present. The OpEM induction program is described and innovative features are discussed. Rules generated by the OpEM induction program are compared to rules obtained by Ross Quinlan's ID3 induction program. Performance of the OpEM induction program when noisy features are present is compared with ID3.

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“…This state-of-the-art graphical, discrete event simulation library works with ExtendTM (by Imagine That, Inc.) and has the power of much more costly, general-purpose simulation products. OpEMCSS, which was developed based on earlier work [5][6][7][8][9][10][11][12][13], is capable of representing several commonly used systems views including queuing, process and resource, and message flow models (see http://ecs.fullerton.edu/~jclymer~.…”

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confidence: 99%

Optimization of Simulated System Effectiveness Using Evolutionary Algorithms

Clymer

1999

SIMULATION

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Using evolutionary algorithms, a search is performed based on a population where each population member consists of a vector of attribute values and a fitness value. A simulation of a system is run, given a particular set of the member attribute values, producing a fitness value. Fitness measures how well the system achieves its mission objectives. If the fitness has a random component, several runs are made to produce average fitness. The procedure is to select the best members from the population based on average fitness and mutate the member attribute values to produce new population members. Since population member attributes can affect process reaction times, wait logic, or decision logic, a search for the best attribute values over 50 to 100 generations can result in optimal fitness. In order to demonstrate the use of evolutionary algorithms in system optimization, a simple inventory system that has a complex fitness surface is considered.

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Discrete event simulation of object movement and interactions

Cited by 18 publications

References 1 publication

Simulation-Based Engineering Of Complex Adaptive Systems

Simulation-Based Engineering Of Complex Adaptive Systems

Induction Of Classification Rules From Noisy Sonar Features

Optimization of Simulated System Effectiveness Using Evolutionary Algorithms

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