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Resonance, 2013
Computers can be used to simulate the operation of complex systems and thereby study their performance. This article introduces you to the technique of discrete event simulation through a simple example.
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Computers can be used to simulate the operation of complex systems and thereby study their performance. This article introduces you to the technique of discrete event simulation through a simple example.
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Parallel Discrete-Event Simulation
IEEE Design & Test of Computers, 1987The availability of commercial multiprocessors gives a new importance to distributed simulation. However, while existing techniques exploit some levels of parallelism in the discrete-event simulation algorithm, they are usually targeted to specific applications and architectures.
Walid Najjar +2 more
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Uncertainty on Discrete-Event System Simulation
ACM Transactions on Modeling and Computer Simulation, 2021Uncertainty Propagation methods are well-established when used in modeling and simulation formalisms like differential equations. Nevertheless, until now there are no methods for Discrete-Dynamic Systems. Uncertainty-Aware Discrete-Event System Specification (UA-DEVS) is a formalism for modeling Discrete-Event Dynamic Systems that include uncertainty ...
Damian Vicino +2 more
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2001
This chapter presents an overview of the techniques used in discrete-event simulation; emphasis is placed on the object-oriented aspects. The process-interaction worldview for modeling is the selected discrete-event simulation approach.
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This chapter presents an overview of the techniques used in discrete-event simulation; emphasis is placed on the object-oriented aspects. The process-interaction worldview for modeling is the selected discrete-event simulation approach.
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1987
It appears to be the rule rather than the exception that usefully detailed stochastic models are sufficiently complex so that it is extremely difficult or impossible to obtain an exact analytic solution. Simulation is essentially a controlled statistical sampling technique that can be used to study complex stochastic systems when analytic techniques do
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It appears to be the rule rather than the exception that usefully detailed stochastic models are sufficiently complex so that it is extremely difficult or impossible to obtain an exact analytic solution. Simulation is essentially a controlled statistical sampling technique that can be used to study complex stochastic systems when analytic techniques do
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Parallel discrete event simulation
Proceedings of the eighth workshop on Parallel and distributed simulation, 1994The field of parallel discrete event simulation is entering a period of self-assessment. Fifteen years of investigation has seen great strides in techniques for efficiently executing discrete event simulations on parallel and distributed machines. Still, the discrete event simulation community at large has failed to recognize much of these results ...
Ernest H. Page, Richard E. Nance
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Parallel Discrete-Event Simulation Applications
Journal of Parallel and Distributed Computing, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Introduction—discrete event simulation
Communications of the ACM, 1990This special section deals with the simulation of complex systems, such as computer, communications, and manufacturing systems—specifically focusing on stochastic discrete event simulation. Such systems are typically (but not always) modeled by a network of queues, in which jobs compete for the system's resources.
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Symbolic Discrete-Event Simulation
1995The event-scheduling view of the discrete-event simulation technique is widely used to model and simulate dynamic systems. This paper presents DMOD, a formalization of this technique. DMOD offers two major advantages. First, it retains the powerful intutions behind this technique, yet makes it easier to specify them.
Sanjai Narain, Ritu Chadha
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Discrete-Event Systems Simulation.
The Journal of the Operational Research Society, 1984Carolyn B. Morgan +2 more
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