Hybrid simulation models combine the high-level project issues of System Dynamics models with the process detail of discrete event simulation models. Hybrid models not only capture the best of both of these simulation paradigms, but they also are able to address new issues that are important in managing complex real-world development projects that neither the System Dynamics nor Discrete Event ...

In this paper we discuss two different approaches to simulation, discrete event simulation and system dynamics. Both have been used widely in the health care domain, although there are fewer applications of system dynamics. The aim of this paper is not to give a comprehensive survey of the literature, but rather to discuss whether the choice of methodology is purely the personal preference of t...

Simulation is often used to solve problems in many areas in the form of problem analysis. Real time graphic applications such as videogames typically use a continuous simulation scheme. This operating scheme has disadvantages that can be avoided by using a discrete event simulator as the application kernel. This paper proposes the integration of a discrete event simulator into a real time graph...

There are two general approaches to computer-based statistical modeling that are used by business managers. (In essence, there are two mathematical approaches to creating the underlying "engines" that execute the models.) One approach is referred to as Systems Dynamics and the other is termed Discrete Event Simulation. Either can be effective in analyzing business problems, though as a generali...

Simulation has been used traditionally to solve other areas problems. Real time applications like videogames use typically a continuous simulation scheme. That way of operation has disadvantages that can be avoided using a discrete event simulator as a game kernel. This paper proposes the integration of a discrete event simulator into real time applications to control the applications simulatio...