Indicative contents

• Introduction
  Systems – discrete and continuous systems, general systems theory, models of systems- variety of modeling approach, concept of simulation, simulation as a decision making tool, types of simulation, Principle of computer modeling- Monte Carlo simulation, Nature of computer modeling, limitations of simulation, area of application.
  
• Random Number Generation
  Techniques for generating random numbers- mid square method, mid product method, constant multiplier technique, additive congruential method, linear congruential method. Tests for random numbers- Kolmogorov-Smirnov test, the Chi-square test
  
• Design and Evaluation of Simulation Experiments
  Problem formulation, data collection and reduction , time flow mechanism, key variables, logic flow charts, starting condition, run size, experimental design consideration, out put analysis, verification and validation of simulation models
  
• Simulation Languages
  Comparison and selection of simulation languages, study of any one-simulation language.
  
• Discrete Event Simulation
  Concepts in discrete –event simulation, development of simulation models for queuing systems, production systems, inventory systems, maintenance and replacement systems, investment analysis and network.
• Programming for discrete event simulation in GPSS, case studies.

Module Resources

• Essential reading
  1. Course notes
     
• Recommended Reading
  1. Khoshnevi. B., ‘Discrete system simulation’, McGraw Hill International edition, 1994
     
  2. Ronald G Askin and Charles R Standridge, ‘Modeling and analysis of manufacturing systems’, John Wiley & Sons, 1993.
     
  3. Gordon G , ‘System Simulation’, Prentice Hall, India, 1991.
     
  4. Thomas J Schriber., ‘Simulation using GPSS’, John Wiley & Sons, 1991.
     
  5. Shannon, R.E., ‘System Simulation – The art and science’, Prentice Hall , India, 1975.
• Journals
• Magazines
• Internet Sites
• Multimedia CDs
• Laboratory