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Simulations and Modeling

Simulations and Modeling focuses on representing real-world systems using mathematical and computational models. It covers model design, simulation techniques, analysis, and validation, enabling prediction, experimentation, and decision-making in scientific, engineering, and business applications.

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TabFlux . Simulation and Modelling . FWU . BSc. CSIT

Simulation and Modelling

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Course Title: Simulation and Modelling

Course No: CSIT.314

Nature of the Course: Theory + Lab

Semester: 5

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 10 + 10

Credit Hours: 3

Course Description

Course Objectives

Course Contents

1. Unit I: Basics of Simulation and Modelling

4 hrs

1.1. Definition of Simulation, When is and is not Simulation Appropriate, Advantages and Disadvantages, Application Areas

1.2. System and System Environment, System Components, Discrete and Continuous Systems

1.3. Model of a System, Types of System Models, Discrete Event System Simulation, Steps in Simulation Study

2. Unit II: Simulation Examples and Principles

4 hrs

2.1. Simulation of Queuing System: Single Channel Queue and Call Center Problem, Simulation of Inventory System: New Dealers Problem, Order up to Level Inventory System

2.2. Concepts in Discrete Event Simulation, The Event Scheduling/Time Advance Algorithm, World Views, Manual Simulation Using Event Scheduling

3. Unit III: Simulation Software

6 hrs

3.1. Categories of Simulation Software, Selection of Simulation Software

3.2. Simulation in GPSS/H, GPSS Building Blocks, Single Server Queue Simulation in GPSS

3.3. Guided Tour Through ARENA, Simple Processing System, Modelling Basic Operations and Inputs, Introduction to Animation

4. Unit IV: Statistical Models

6 hrs

4.1. Review of Terminology and Concept, Useful Statistical Models

4.2. Discrete Distributions: Binomial, Geometric and Poisson Distribution; Continuous Distributions: Uniform, Exponential, Gamma, Normal, and Triangular Distribution

4.3. Poisson Process, Properties of Poisson Process, Non-stationary Poisson Process, Empirical Distributions

5. Unit V: Continuous System Simulation

5 hrs

5.1. Characteristics of Queuing Systems, Types of Queues, Queuing Notation

5.2. Long-Run Measures of Performance of Queuing Systems, Markov Models

5.3. Differential and Partial Differential Equations in Simulating Continuous Systems

6. Unit VI: Random Numbers

7 hrs

6.1. Properties of Random Numbers, Generation of Pseudo-random Numbers

6.2. Random Number Generation Techniques: Linear Congruential Method, Combined Linear Congruential Generator, Random Number Streams

6.3. Test for Random Numbers: Frequency Tests, Uniformity Test, Test for Autocorrelation

6.4. Random Variate Generation: Inverse Transform Technique-Exponential, Uniform, Empirical Continuous and Discrete Distributions, Acceptance-Rejection Technique-Poisson Distribution, Non-stationary Poisson Process, Gamma Distribution

7. Unit VII: Input Modelling, Verification and Validation

8 hrs

7.1. Data Collection, Identifying Distribution with Data, Parameter Estimations

7.2. Goodness-of-fit Tests: Chi-Square Test, Chi-Square Test with Equal Probabilities, p-values and Best Fits

7.3. Selecting Input Models without Data, Multi-Variate and Time-Series Input Models

7.4. Model Building, Verification, and Validation, Verification of Simulation Models, Calibration and Validation of Models

8. Unit VIII: Output Analysis

5 hrs

8.1. Types of Simulation with respect to Output Analysis, Stochastic Nature of Output Data

8.2. Measures of Performance and their Estimation: Point Estimation, Confidence Interval Estimation

8.3. Output Analysis for Terminating Simulations: Confidence Interval with Specified Precision, Quantiles

8.4. Output Analysis for Steady-State Simulations: Bias Initialization, Error Estimation and Replication Method

Laboratory Works

1.Manual Simulation of Queuing and Inventory Systems
2.Simulation Using GPSS
3.Simulation Using ARENA
4.Statistical Distribution Fitting
5.Random Number Generation and Testing
6.Random Variate Generation
7.Input Data Modelling and Goodness-of-fit Tests
8.Output Analysis
9.Group Project: Real World Simulation

Text Books

1.Banks, Carson, Nelson, and Nicol, Discrete-Event Simulation, Fourth Edition, 2005, Prentice Hall.
2.W. David Kelton, Randall P. Sadowski and Nancy B. Swets, Simulation with Arena, Fifth Edition, 2010, McGraw Hill.

Reference Books

1.Geoffrey Gordon, System Simulation, Second Edition, 1978, Prentice Hall of India.
2.Thomas J. Schriber, An Introduction to Simulation Using GPSS/H, 1991, Wiley Edition.

Notes:

Source:

A simulation is a computer model that mimics the operation of a real or proposed system. Simulation is a commonly-used and practical technique for modeling and analyzing real world systems in order to make more effective decisions. This course is designed to teach students the processes, tools, and techniques for performing effective simulation analyses, focusing on how simulations work, how to collect and analyze input data, how to build and validate simulation models, and how to interpret simulation output.

To design simulation models; to design simulation studies; to analyze simulation output; to collect and analyze input data; to incorporate knowledge from other disciplines in simulation studies.

Students practice laboratory exercises using simulation and modeling packages such as GPSS and ARENA. Students may also develop their own simulators using general purpose high level programming languages such as C or C++. A group project simulating a real world problem is highly recommended. The lab work should be practiced for a minimum of 3 lab hours per week.

This syllabus follows the official CSIT curriculum of Far Western University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative.