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Lecture Notes of
Modeling and Simulation
7th Sem IT
BCS-408
Module I
MODELING & SIMULATION (3-1-0)
Cr.-4
(10 Lectures)
Inventory Concept: The technique of Simulation. : 1 class
Major application areas, concept of a System.
: 1 class
Environment. : 1 class
Continuous and discrete systems.
: 1 class
Systems modeling, types of models. : 1 class
Progress of a Simulation Study.
: 1 class
Monte Carlo Method. : 1 class
Comparison of Simulation and Analytical Methods. : 1 class
Numerical Computation Technique for discrete and continuous models.
Continuous System Simulation. ;1 class
Revision
Module II
: 1 class
(12 Lectures)

Probability Concepts in Simulation: 2 classes
Stochastic variables, Discrete and Continuous Probability Functions.
2 classes
Numerical evaluation of continuous probability functions, continuous uniformly distributed
random numbers.
: 2 classes
Random Number Generators – Linear congruential Generator, Mid Square Method,
Multiplicative Congruential generator, rejection Method. : 2 classes
Testing of random Numbers. : 2 classes
Generation of Stochastic variants. : 1 class
Arrival Patterns Service times.
: 1 class
Revision
Module III
(10 Lectures)
Discrete System Simulation and GPSS: Discrete Events, Representation of Time, generation of
arrival patterns.
: 2 classes
Fixed time step versus next event simulation, Simulation of a
Telephone System, Delayed calls . : 2 classes
Introduction to GPSS: Creating and moving transactions, queues. : 2 classes
Facilities and storages, gathering statistics, conditional transfers, program control statements,
priorities and parameters.
: 2 classes
Standard numerical attributes, functions, gates, logic switches and tests, Variables, Select and
Count. : 2 classes
Revision
Module IV
(10 Lectures)
Simulation Languages and Practical Systems
: 1 class
Continuous and discrete systems languages, factors in the section of discrete systems simulation
language.
; 2 classes
Computer model of queuing, inventory and scheduling systems. : 2 classes
Design and Evaluation of simulation Experiments: Length of simulation runs, validation,
variance reduction techniques.
: 2 classes
Experimental layout, analysis of simulation output,
Recent trends and developments.
: 1 class
Revision
Books:
1.
2.
System Simulation – Geoffrey Gordon, 2nd Edition, PHI
System Simulation with Digital computer – Narsingh Deo, PHI

Module-I
Objectives:
To give an overview of the course (Modeling & simulation).
Define important terminologies.
Classify systems/models
System:
any set of interrelated components acting together to achieve a common objective.
Examples:
1. Battery
•
Consists of anode, cathode, acid and other omponents.
• These components act together to achieve one objective like preserving electricity.
2. University
• Consists of professors, students and employees.
• These objects act together to achieve the objective of teaching & learning process.
A system consists of
•
Inputs
Elements that cause changes in the systems variables.
•
Outputs
Response
• Systems (process)
Defines the relationship between the inputs and outputs
Some Possible Inputs
• Inlet flow rate
•
Temperature of
entering material

•
Concentration of
entering material
Some Possible Outputs
• Level in the tank
•
Temperature of
material in tank
• Outlet flow rate
•
Concentration of material in tank
Qn: What inputs and outputs are needed when we want to model the Inventory
Control System?
Model: A model describes the mathematical relationship between inputs and outputs.
Simulation: is the process of using the mathematical model to determine the
response of the system in different situations in a Computer system.
Classification of Systems
Systems can be classified based on different criteria:
•
•
Spatial characteristics: lumped & distributed
•
Continuity of the time
variable: Continuous, discrete-time
•
Quantization of dependent
variable: Quantized & Nonquantized
•
Parameter variation: time varying & fixed (time-invariant)
Superposition principle: linear & nonlinear
Continuous-time System:
• The signal is defined for all t in an interval [ti, tf]
Discrete-time System:
• The signal is defined for a finite number of time points {t0, t1,…}
A system is linear:
• if it satisfies the super position principle.
• A system satisfies the superposition principle if the following conditions are satisfied:
1. Multiplying the input by any constant, multiplies the output by the same constant.
2. The response to several inputs applied simultaneously is the sum of individual
response to each input applied separately.

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