B.E. Computer Science & Engineering:Simulation and Modeling

First Year: Semester I

Mathematics I
Engineering graphics
Computer Programming
Physics
Solid Mechanics
Communication Skill

First Year: Semester-II

Mathematics II
Manufacturing Process
Chemistry
Electrical and Electronic Science
Thermodynamics
Organizational Behavior

Second Year- Semester - I

Measurement Science and Techniques
Optimization Techniques
Semiconductor Devices
Data Structures
Discrete Mathematical Structures
Digital Electronic Circuits
Human Values, Ethics and IPR

Second Year- Semester – II

Numerical and Statistical Methods
Electrical Engineering Materials
Computer System Architecture
Principles of Programming Languages
Analysis and Design of Information Systems
Operating Systems
Environmental Studies


Third Year- Semester – I

Object Oriented Programming
Theory of Computation
Computer Networks
Data Base Management Systems
Software Engineering
Microprocessors
Summer Training

Third Year- Semester – II

Total Quality Management
Algorithm Analysis and Design
Software Project Management
Internet and Web Technologies

Fourth Year- Semester – I

Engineering Economics
System Software
Compiler Construction
Computer Graphics
Artificial Intelligence

Fourth Year- Semester – II

Project Semester
Project
Industrial Training(6 weeks)


Simulation and Modeling

Introduction: Objectives of Modeling, System Theory and State Variables.

Type of Models: Analytic, Simulation, Measurement, Analytic Modeling, Probability Theory, Random Variables, Poisson Process, Markov Chains.

Queuing Theory: Little�s Law, M/M/1, M/M/1/k, M/M/C Queuing Models, M/G/1 [Impact Variation in Service Times].

Petrinets: Stochastic Petrinets [ SPN ] , GSPN .

Simulation Modeling: Continuous & discrete Event Simulation, Monte Carlo Simulation, Pseudo Random Number Generation, Non-Uniform Random Variable Generation, Simulation Language Features : Simpack, GPSS, GASP IV, CSIM, Estimation of Simulation Outputs/Output Matrix, Confidence Intervals, Regenerative Simulation, Method of Batch Means.

Case Studies: Analytic vs. Simulation Models, Application to Operating Systems, Data Bases, Networks Architecture.

Laboratory work: Write programs to generate random numbers using seed method, to test randomness of two numbers generated using frequency test, uniformity test, to implement queue using M/M/1 and M/M/1/K models, to implement bomber fighter problem, to implement inventory control system keeping storage cost, lost business etc in mind, to implement a chemical reactor problem, to implement various features of various simulation languages such as GPSS, GASP, CSIM. .