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Simulation

DATE: 4th - 8th July 2011
LOCATION: University of Warwick

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. Main contributors: Professor Stewart Robinson (course leader, Warwick), Professor Russell Cheng (Southampton), Professor Ruth Davies (Warwick), Professor Mike Pidd (Lancaster). PRE-REQUISITES An understanding of the basic principles of statistics including confidence intervals and hypothesis testing. It would be useful to read parts of the following books: Statistical Aspects

  • Krzanowski, W.J. 1998. An Introduction to Statistical Modelling. London: Arnold
  • Makridakis, S. Wheelwright, S.C. and Hyndman, R.J. 1998. Forecasting. Methods and Applications. New York, Wiley.

Simulation Concepts

  • Pidd, M. (2005). Computer Simulation in Management Science, 5th ed. Wiley, Chichester, UK.
  • Robinson, S. (2004). Simulation: The Practice of Model Development and Use. Wiley, Chichester, UK.

Please note that material for preliminary reading and preparation will be available on-line in advance of the course. This will mainly concern the basics of statistical aspects of simulation. AIMS 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 understand the nature of, and approaches to, input data modelling
  • To be able to analyse the output from a simulation model using appropriate methods

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

PRINCIPAL TOPICS OF STUDY

  1. Principles of Simulation
    1. Introduction to simulation methods (Monte Carlo, discrete-event, system dynamics and agent based simulation) and simulation software
    2. Generating random variates
    3. Computing aspects of simulation
    4. Distributed simulation
    5. Simulation studies and the simulation modelling life-cycle
    6. Model validation
  2. Variance Reduction
    1. Random number generation
    2. Variance reduction methods
  3. Introduction to Output Analysis
    1. Analysis of a single scenario – obtaining accurate results
    2. Basics of comparing multiple scenarios
  4. Experimental Design and Analysis
    1. Time series analysis
    2. Experimental design
    3. Metamodelling
    4. Simulation optimisation
    5. Bootstrapping (for input data modelling as well)