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Simulation, Loughborough University - 10th - 14th July 2017

*Please note we are unable to offer en suite accommodation for this course.  All rooms will have shared facilities*


Simulation is one of the most widely used operational research techniques. It involves the development of an imitation on a computer of the system under study, followed by experimentation to understand and investigate improvements to the system. This course provides an understanding of simulation, with a focus on the mathematical and statistical principles of stochastic simulation modelling. The main technique of interest is discrete-event simulation, although other simulation techniques will be introduced.


An understanding of the basic principles of statistics including confidence intervals and hypothesis testing.
It would be useful to read some parts of the following books.

Statistical Aspects
Krzanowski, W.J. 2010. An Introduction to Statistical Modelling. Wiley, Chichester, UK.
Makridakis, S. Wheelwright, S.C. and Hyndman, R.J. 1998. Forecasting: Methods and Applications. 3rd ed., New York, Wiley.

Simulation Concepts
Pidd, M. (2005). Computer Simulation in Management Science, 5th ed
. Wiley, Chichester, UK.
Robinson, S. (2014). Simulation: The Practice of Model Development and Use. 2nd ed., Palgrave, UK.

Banks et al. (2001) Discrete Event System Simulation by (3rd edition), Prentice Hall. (Chapter 12)
Law (2006) Simulation Modeling and Analysis (4th edition). (Chapters 10-12)


The aim of the course is to provide an understanding of the mathematical and statistical principles of stochastic simulation modelling. The specific objectives are as follows:

To understand the alternative simulation methods and the requirements for simulation studies

  • To develop skills in simulation modelling and simulation computer packages

  • To understand the nature of, and approaches to, input data modelling

  • To be able to analyse the output from a simulation model using appropriate methods


Subject Knowledge and Understanding
On completion of the course students will be expected to:

  • Understand the basic principles of simulation and performing simulation studies.
  • Understand the basic theory of simulation analysis including input data analysis, experimentation and output analysis.

  • Understand the key stages in developing and using simulation models

  • To be aware of the strengths and limitations of the approaches covered.

    Intellectual Skills
    On completion of the course students will be expected to:

  • Be able to develop and use a simulation model for a given problem situation.
  • Be able to evaluate the quality of a simulation analysis 

Practical Skills
On completion of the course students will be expected to:

  • Be familiar with the use of a simulation software package (SIMUL8).
  • Be able to follow an appropriate life-cycle for the development and use of simulation models

  • Be familiar with the use of statistical analysis software (e.g. Excel, Minitab, SPSS).


1. Principles of Simulation
1.1 Introduction to simulation methods (Monte Carlo, discrete-event, system dynamics and agent based simulation) and simulation software
1.2 Generating random variates
1.3 Computing aspects of simulation
1.4 Simulation studies and the simulation modelling life-cycle
1.5 Model validation

2. Random Sampling
2.1 Random number generation
2.2 Sampling from distributions
2.3 Variance reduction methods

3. Introduction to Output Analysis
3.1 Analysis of a single scenario obtaining accurate results
3.2 Basics of comparing multiple scenarios

4. Experimental Design and Analysis
4.1 Time series analysis
4.2 Experimental design
4.3 Metamodelling
4.4 Simulation optimisation
4.5 Bootstrapping (for input data modelling as well)


Material intended for preliminary reading discussing basics of the statistical aspects of simulation.

  • Start Date: 10/07/2017
  • End Date: 14/07/2017
  • Location: Loughborough University
  • Address: Leicestershire
  • Postcode: LE11 3TU
Sorry, this course is unavailable at this time