Principle of computer modeling and simulation, Monte Carlo simulation. Nature of
computer modelling and simulation. Limitations of simulation, areas of application.
System and environment – components of a system – Discrete and continuous
systems. Models of a system – A variety of modelling approaches.
Random number generation, technique for generating random numbers – Midsquare
method – The midproduct method – Constant multiplier technique – Additive
congruential method – Linear congruencies method – Tests for random number –
The Kolmogorov Smirnov test – The chi-square test. Random variable generation –
Inverse transform technique – Exponential distribution – Uniform distribution –
Weibull distribution, empirical continuous distribution – Generating approximate normal
variates. Empirical discrete distribution – Discrete uniform distribution – Poisson
distribution – Geometric distribution – Acceptance – Rejection technique for Poisson
distribution – Gamma distribution.
Design and evaluation of simulation experiments – Input – Output analysis – Variance
reduction technique – Verification and validation of simulation models. Discrete
event simulation – Concepts in discrete – event simulation – Manual simulation
using event scheduling, single channel queue, two server queue, simulation of
inventory problems. Simulation languages – GPSS – SIMSCRIPT – SIMULA –
Programming for discrete event systems in GPSS and C. Case Study: Simulation of
LAN – Manufacturing system – Hospital management system.