Page 61 - DCAP601_SIMULATION_AND_MODELING

Sandeep Kumar, Lovely Professional University
Unit 4: Discrete System Simulation (I)

Unit 4: Discrete System Simulation (I) Notes

CONTENTS
Objectives
Introduction
4.1 Discrete System Simulation

4.1.1 Components of a Discrete-event Simulation
4.1.2 Application Areas/Common Uses
4.2 Fixed Time Step vs Event-to-Event Model
4.3 Summary
4.4 Keywords
4.5 Self Assessment
4.6 Review Questions

4.7 Further Readings

Objectives

After studying this unit, you will be able to:

 Understand fixed time step
 Describe event to event model

Introduction

Discrete Event Simulation (DES) concerns the of a system as it evolves over time by representing
the changes as separate events. This is the opposite of Continuous Simulation where the system
evolves as a continuous function (differential). Fixed time step and even to event are the models
for moving a system during time. The simulation needs to generate random variables of various
kinds, depending on the system model. References to Monte Carlo simulation are often
encountered in the and simulation literature.

4.1 Discrete System Simulation

In discrete-event simulation, the operation of a system is represented as a chronological sequence
of events. Each event occurs at an instant in time and marks a change of state in the system.

Example: If an elevator is simulated, an event could be “level 6 button pressed”, with the
resulting system state of “lift moving” and eventually (unless one chooses to simulate the
failure of the lift) “lift at level 6”.

A common exercise in learning how to build discrete-event simulations is to model a queue,
such as customers arriving at a bank to be served by a teller. In this example, the system entities
are CUSTOMER-QUEUE and TELLERS. The system events are CUSTOMER-ARRIVAL and
CUSTOMER-DEPARTURE. (The event of TELLER-BEGINS-SERVICE can be part of the logic of
the arrival and departure events.) The system states, which are changed by these events, are

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