Introduction
One of the most powerful modeling tools in the operations research analyst’s toolbox is stochastic (or Monte Carlo) simulation, which provides the ability to study complex stochastic systems in great detail using a computer program. Simulation models complement analytical models that require many simplifying assumptions, and in many situations, simulation provides the only way to analyze a system. Stochastic discrete-event systems are systems whose state changes upon the occurrence of discrete events, usually at stochastic times (Cassandras and Lafortune 2010). For example, in a queueing system, the state of the system includes the queue lengths, which change at discrete points in time when arrivals or departures occur. Discrete-event systems can be contrasted with continuous-time, continuous-state systems whose state changes continuously over time, with dynamics usually driven by differential equations, e.g., the motion of particles in a fluid. Discrete-event systems are...
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Fu, M.C., Gross, D. (2013). Simulation of Stochastic Discrete-Event Systems. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_959
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