Fidelity, Resolution, Accuracy, and Uncertainty

Abstract

A discrete-event simulation (DES) is characterized by discrete changes in the sim-ulation’s state as the simulation evolves over time. Examples of systems that might be evaluated using DES include: queueing systems, such as a bank service counter, where customers arrive occasionally and may wait in lines for service; manufacturing systems, where parts are processed in various sequences at differ-ent stations; and inventory systems, where random quantities of a certain product are purchased by customers each day at a store, which in turn may cause additional actions along the product supply chain. These systems are driven by various events that occur at discrete times and change the state of the simulation. Event times correspond to, for example: customer arrivals, customer departures from a server or from the system, machine breakdowns, and even an “end-of-simulation” event. In this chapter, we will give a high-level description of how a DES works, followed by a running example illustrating the salient concepts, and then a brief discussion of specialty DES software.