SIMULATION AND MODELLING
ICS 2307 SIMULATION AND MODELLING
Course Outline
Systems modelling – discrete event simulation
Design of simulation experiments simulation
Language probability and distribution theory
Statistical estimation, inference and random number generators
Sample event sequences for random number generation
Translation of models for simulation application
References
Simulation modelling and analysis
Introduction
Computers can be used to imitate (simulate) the operations of various kinds of real world facilities or processes. The facility or process of interest is usually called a system and in order to study it scientifically, we often have to make a set of assumptions about how it works.
These assumptions which usually take the form of mathematical or logical relationships constitute a model that is used to try to gain some understanding of how the corresponding system behaves. If the relationships that propose the model are simple enough, it may be possible to use mathematical methods to obtain exact information on questions of interest. This is called an analytic solution. However, most real world systems are too complex to allow realistic models to be evaluated analytically. Such models must be studied by means of simulation.
In a simulation, we use a computer to evaluate a model numerically and data is gathered in order to estimate the desired true characteristics of the model. For example: consider a manufacturing firm that is contemplating building a large extension onto one of its plants but is not sure if the potential gain in productivity would justify the construction costs. A careful simulation study would shed some light on the question by simulating the operation of the plant as it currently exists and as it would be if the plant were expanded.
Simulation has been found to be a useful tool in a number of areas:
i. Designing and analysing manufacturing systems. ii. Evaluating hardware and software requirements for a computer system.
iii.
Course Outline
Systems modelling – discrete event simulation
Design of simulation experiments simulation
Language probability and distribution theory
Statistical estimation, inference and random number generators
Sample event sequences for random number generation
Translation of models for simulation application
References
Simulation modelling and analysis
Introduction
Computers can be used to imitate (simulate) the operations of various kinds of real world facilities or processes. The facility or process of interest is usually called a system and in order to study it scientifically, we often have to make a set of assumptions about how it works.
These assumptions which usually take the form of mathematical or logical relationships constitute a model that is used to try to gain some understanding of how the corresponding system behaves. If the relationships that propose the model are simple enough, it may be possible to use mathematical methods to obtain exact information on questions of interest. This is called an analytic solution. However, most real world systems are too complex to allow realistic models to be evaluated analytically. Such models must be studied by means of simulation.
In a simulation, we use a computer to evaluate a model numerically and data is gathered in order to estimate the desired true characteristics of the model. For example: consider a manufacturing firm that is contemplating building a large extension onto one of its plants but is not sure if the potential gain in productivity would justify the construction costs. A careful simulation study would shed some light on the question by simulating the operation of the plant as it currently exists and as it would be if the plant were expanded.
Simulation has been found to be a useful tool in a number of areas:
i. Designing and analysing manufacturing systems. ii. Evaluating hardware and software requirements for a computer system.
iii.