Page 63 - DCAP601_SIMULATION_AND_MODELING

Unit 4: Discrete System Simulation (I)

4.1.2 Application Areas/Common Uses Notes

Well-known examples of Simulation are Flight Simulators, Fleet Management and Business
games. However, there are a large number of potential areas for Discrete Event Simulation. One
of the main areas currently being explored is in designing new manufacturing areas, especially
where high capital investment is involved. For example, if a company wishes to build a new
production line, then the line can be first simulated to assess feasibility and efficiency. The
diagram below shows the key stages in using Discrete Event Simulation. It can be noted that this
bears a strong resemblance to other simulation techniques and other analysis program
development methodologies (prototype method) [Somerville, 1992].

Figure 4.1: Stages used in Discrete event Simulation

Key Principles

Although, discrete event simulation could conceivably be carried out by hand it can be
computationally intensive, therefore will invariably involve computers and software. The
software could be a high level programming language such as Pascal or a specialised event/data
driven application, such as iBright Ltd’s ‘baseSim’ (Monte Carlo Simulation). The five key features
found in the software simulation model are:

1. Entities: Representations of real-life elements e.g. in manufacturing these could be parts
or machines.
2. Relationships: Link entities together e.g. a part may be processed by a machine.

3. Simulation Executive: Responsible for controlling the time advance and executing discrete
events.
4. Random Number Generator: Helps to simulate different data coming into the simulation
model. Important that the random data can be reproduced in different simulation runs.
5. Results & Statistics: Important in validating the model and for providing performance
measures.
The simulation executive may operate in one of two manners [Ball, 1996]:
1. Time Slicing: Advances the model by a fixed amount each time, regardless of the absence
of any events to carry out.
2. Next Event: Advances the model to the next event to be executed, regardless of the time
interval. This method is more efficient than Time Slicing, especially where events are
infrequent, but can be confusing when being represented graphically (processes that take
different times will appear to happen in the same time frame if the stop event is the next
event after the start event).

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