# SIMULATION AND MODELING SYLLABUS

December 11, 2011
Assurance Syllabus, cms, it, mu engineering notes, PPT, sem 7, SIMULATION AND MODELING
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**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

simulation study.

**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.

**Text Book:**

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**