Page 242 - DCAP601_SIMULATION_AND_MODELING

Simulation and Modelling Nisha Sethi, Lovely Professional University

Notes Unit 13: Simulation Languages (I)

CONTENTS
Objectives
Introduction

13.1 Continues and Discrete Systems Simulation Languages
13.1.1 CODIS Framework
13.1.2 Discrete System Simulation
13.1.3 Example of Discrete-event Simulation (DES) – Models of Plasmodium
Falciparum Malaria
13.2 Continuous Simulation Languages
13.3 Summary

13.4 Keywords
13.5 Self Assessment
13.6 Review Questions
13.7 Further Readings

Objectives

After studying this unit, you will be able to:
 Understand continuous and discrete simulation languages

 Discuss continuous simulation languages
Introduction

A computer simulation language describes the operation of a simulation on a computer. There
are two major types of simulation: Continuous and discrete event though more modern
languages can handle combinations. Most languages also have a graphical interface and at least
simple statistical gathering capability for the analysis of the results. An important part of discrete-
event languages is the ability to generate pseudo-random numbers and variates from different
probability distributions.

13.1 Continues and Discrete Systems Simulation Languages

In models for discrete event dynamic systems (i.e., DEDS models) state changes occur at particular
points in time whose values are not known a priori. As a direct consequence, (simulated) time
advances in discrete ‘jumps’ that have unequal length.
In contrast, with models that emerge from the domain of continuous time dynamic systems (i.e.,
CTDS models), state changes occur continuously (at least in principle) as time advances in a
continuous fashion over the length of the observation interval. It must, however, be realities
introduced by the computational process. It is simply infeasible for any practical procedure to
actually yield data at every value of time within the continuum of the observation interval.

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