We review techniques for optimizing stochastic discrete-event systems via simulation. We
discuss both the discrete parameter case and the continuous parameter case, but …

Modeling and simulation are a powerful and effective problem-solving methodology to study
how complex real-world systems behave over time. In the literature, many authors have …

After going through all the previous chapters, it would be natural for readers to conclude that
DES are inherently complex and hard to analyze, regardless of the modeling framework …

A discrete event system (DES) is a dynamic system that evolves in accordance with the
abrupt occurrence, at possibly unknown irregular intervals, of physical events. Such systems …

Consider a computer system having a CPU that feeds jobs to two input/output (I/O) devices
having different speeds. Let θ be the fraction of jobs routed to the first I/O device, so that 1-θ …

A discrete-event system is a system whose behavior can be described by means of a set of
time-consuming activities, performed according to a prescribed ordering. Events correspond …

Statistical performance evaluation has assumed an increasing amount of importance as we
seek to design more and more sophisticated communication and information processing …

Discrete-event dynamic systems (DEDs) permeate our world, being of great importance in
modern manufacturing processes, transportation and various forms of computer and …

In many industrial applications, large centralized software systems are not as effective as
distributed networks of relatively simpler computerized agents. For example, to compete …

We present a new method of obtaining derivatives of expectations with respect to various
parameters. For example, if λ is the rate of a Poisson process, NT is the number of Poisson …