QLTY09031 2013 System Simulation Modelling and Analysis

Describe the fundamentals of simulation and the techniques for developing simulation models.

Use simulation as a decision support tool.

Translate a business problem into a simulation model.

Formulate an appropriate and correct discrete event simulation model of a system at an appropriate level of detail.

Use an Excel spreadsheet with add-ins, e.g. @Risk, to build simulation models.

Use SIMUL8 to model and solve a simulation problem.

Identify the best probability distribution to describe the outcomes of a random variable.

Generate random variates for discrete and continuous distributions.

Analyse and interpret the results of a simulation model for making a business decision.

Real world examples, case studies and published peer reviewed papers will be utilised, where possible.

Continuous Assessment

1.    Project work - Design and analyse ones own Simulation                   20%.

Final Examination                

1.    One written 2.5 hour paper                                                            40%

2.    One computer based 2.5 hour exam involving @Risk & Simul8          40%

Basic simulation modelling

Introduction to Simulation. When is simulation appropriate. When is simulation not appropriate. Advantage and disadvantages of simulation. Areas of application. Components of a system. Discrete event and continuous simulation.

Simulation Examples

Illustration of general area of application. For example, simulation of a queueing system.

Spreadsheet Modelling

Developing a spreadsheet model. Concepts. Use of Solver. Optimisation modelling. Sensitivity analysis. Simulation of an Inventory system. Lead-time simulation. Supply chain  and outsourcing simulation. Warranty model simulation.

Simulation Principles

Dynamic and stochastic systems. Concepts in discrete event simulation. Developing a discrete event model.

Simulation Software

Comparison of simulation packages with programming languages. Classification of simulation software. Desirable software features. General-purpose simulation packages. Object oriented simulation. Trends in simulation software. Focus on SIMUL8 and @Risk (Excel add-on).

Statistical Models in Simulation

Review of basic probability and statistical distributions. Discrete distributions. Continuous distributions. Empirical distributions. Models for arrival processes e.g. Poisson process.

Queueing Models

Characteristics of queueing systems. Queueing notation. Little's formula. Long run measures of performance of queueing systems. Server utilisation. Spreadsheet queueing simulation models.

Random Number Generators

Properties. Generation of pseudo-random numbers. Techniques for Generating random numbers. Linear congruential method. Combined linear congruential generators. Tests for random number.

Generating Random Variates

General programming methods for generating random variates. Inverse transform technique. Convolution method. Acceptance-Rejection method.

Input Modelling

Developing a useful model of input data. Collecting data, Identifying a probability distribution, parameter estimation, Goodness of fit testing. Selecting input models without data.

Model Verification and Validation

Verification of simulation models. Calibration and validation of models. Face validity. Validation of model assumptions. Input-output validation.

Output Analysis

Examination of data generated by simulation. Stochastic nature of output data. Output analysis for a single model. Measures of performance and their estimation. Comparing alternative system configurations.