SIMULATION AND MODELING SYLLABUS
Prerequisite: Probability and Statistics
Objective: The objective of this course is to teach students methods for modeling of
systems using discrete event simulation. Emphasis of the course will be on modeling
and on the use of simulation software. The students are expected to understand the
importance of simulation in IT sector, manufacturing, telecommunication, and service
industries etc. By the end of the course students will be able to formulate simulation
model for a given problem, implement the model in software and perform simulation
analysis of the system.
1. Introduction to Simulation and Modeling: Simulation – introduction, appropriate
and not appropriate, advantages and disadvantage, application areas, history of
simulation software, an evaluation and selection technique for simulation software,
general – purpose simulation packages. System and system environment, components
of system, type of systems, model of a system, types of models and steps in
2. Manual Simulation of Systems: Simulation of Queuing Systems such as single
channel and multi channel queue, lead time demand, inventory system, reliability
problem, time-shared computer model, job-shop model.
3. Discrete Event Formalisms: Concepts of discrete event simulation, model
components, a discrete event system simulation, simulation world views or
formalisms, simulation of single channel queue, multi channel queue, inventory
system and dump truck problem using event scheduling approach.
4. Statistical Models in Simulation: Overview of probability and statistics, useful
statistical model, discrete distribution, continuous distribution, empirical distribution
and Poisson process.
5. Queueing Models: Characteristics of queueing systems, queueing notations, long run
measures of performance of queueing systems, Steady state behavior of Markovian
models (M/G/1, M/M/1, M/M/c) overview of finite capacity and finite calling
population models, Network of Queues.
6. Random Number Generation: Properties of random numbers, generation of true
and pseudo random numbers, techniques for generating random numbers, hypothesis
testing, various tests for uniformity (Kolmogorov-Smirnov and chi-Square) and
independence (runs, autocorrelation, gap, poker).
7. Random Variate Generation: Introduction, different techniques to generate random
variate:- inverse transform technique, direct transformation technique, convolution
method and acceptance rejection techniques.
8. Input Modeling: Introduction, steps to build a useful model of input data, data
collection, identifying the distribution with data, parameter estimation, suggested
estimators, goodness of fit tests, selection input model without data, covariance and
correlation, multivariate and time series input models.
9. Verification and Validation of Simulation Model: Introduction, model building,
verification of simulation models, calibration and validation of models:- validation
process, face validity, validation of model, validating input-output transformation, ttest,
power of test, input output validation using historical data and Turing test.
10. Output Analysis: Types of simulations with respect to output analysis, stochastic
nature of output data, measure of performance and their estimation, output analysis of
terminating simulators, output analysis for steady state simulation.
11. Case Studies: Simulation of manufacturing systems, Simulation of Material
Handling system, Simulation of computer systems, Simulation of super market,
Cobweb model, and any service sectors.
Banks J., Carson J. S., Nelson B. L., and Nicol D. M., “Discrete Event System
Simulation”, 3rd edition, Pearson Education, 2001.
SIMULATION AND MODELING Formulae Notes