Syllabus as per University of Mumbai: Computer Simulation and Modeling
1. UNIT - I Introduction to Simulation
Introduction to Simulation
Examples simulation
General Principles : 15
2. UNIT - II Mathematical & Statistical Models in simulation
Statistical Models in simulation
Queuing Models : 8
3. UNIT - III Random Numbers
Random Number Generation, Testing random numbers (Refer to Third edition)
Random Variate Generation: Inverse transform technique, Direct Transformation for the Normal Distribution, Convolution Method, Acceptance-Rejection Technique (only Poisson Distribution) : 9
4. UNIT – IV Analysis of Simulation data
Input Modeling
Verification, Calibration and Validation of Simulation models
Estimation of absolute performance : 12
5. UNIT V Application
Case study:
Processor and Memory simulation
Manufacturing & Material handling : 4
Text Books:
1. Discrete Event System Simulation; Third Edition, Jerry Banks, John Carson, Barry Nelson, and David M. Nicol, Prentice-Hall
2. Discrete Event System Simulation; Fifth Edition, Jerry Banks, John Carson, Barry Nelson, and David M. Nicol, Prentice-Hall
References:
1. System Modeling & Analysis; Averill M Law, 4th Edition TMH.
2. Principles of Modeling and Simulation; Banks C M , Sokolowski J A; Wiley
3. System Simulation ; Geoffrey Gordon ; EEE
4. System Simulation with Digital Computer; Narsing Deo, PHI
Course Objectives:
This course presents an introduction to discrete event simulation systems. Emphasis of the course will be on modeling and the use of simulation languages/software to solve real world problems in the manufacturing as well as services sectors.
The course discusses the modeling techniques of entities, queues, resources and entity transfers in discrete event environment. The course will teach the students the necessary skills to formulate and build valid models, implement the model, perform simulation analysis of the system and analyze results properly.
The “theory” of simulation involves probability and statistics, thus a good background in probability and statistics is a required prerequisite
Course Outcomes:
1. Understand the meaning of simulation and its importance in business, science, engineering, industry and services
2. Identify the common applications of discrete-event system simulation.
3. Practice formulation and modeling skills.
4. Understand simulation languages
5. Ability to analyze events and inter-arrival time, arrival process, queuing strategies, resources and disposal of entities
6. An ability to perform a simulation using spreadsheets as well as simulation language/package
7. Ability to generate pseudorandom numbers using the Linear Congruential Method
8. Ability to perform statistical tests to measure the quality of a pseudorandom number generator
9. Ability to define random variate generators for finite random variables
10. Ability to analyze and fit the collected data to different distributions
1. UNIT - I Introduction to Simulation
Introduction to Simulation
Examples simulation
General Principles : 15
2. UNIT - II Mathematical & Statistical Models in simulation
Statistical Models in simulation
Queuing Models : 8
3. UNIT - III Random Numbers
Random Number Generation, Testing random numbers (Refer to Third edition)
Random Variate Generation: Inverse transform technique, Direct Transformation for the Normal Distribution, Convolution Method, Acceptance-Rejection Technique (only Poisson Distribution) : 9
4. UNIT – IV Analysis of Simulation data
Input Modeling
Verification, Calibration and Validation of Simulation models
Estimation of absolute performance : 12
5. UNIT V Application
Case study:
Processor and Memory simulation
Manufacturing & Material handling : 4
Text Books:
1. Discrete Event System Simulation; Third Edition, Jerry Banks, John Carson, Barry Nelson, and David M. Nicol, Prentice-Hall
2. Discrete Event System Simulation; Fifth Edition, Jerry Banks, John Carson, Barry Nelson, and David M. Nicol, Prentice-Hall
References:
1. System Modeling & Analysis; Averill M Law, 4th Edition TMH.
2. Principles of Modeling and Simulation; Banks C M , Sokolowski J A; Wiley
3. System Simulation ; Geoffrey Gordon ; EEE
4. System Simulation with Digital Computer; Narsing Deo, PHI
Course Objectives:
This course presents an introduction to discrete event simulation systems. Emphasis of the course will be on modeling and the use of simulation languages/software to solve real world problems in the manufacturing as well as services sectors.
The course discusses the modeling techniques of entities, queues, resources and entity transfers in discrete event environment. The course will teach the students the necessary skills to formulate and build valid models, implement the model, perform simulation analysis of the system and analyze results properly.
The “theory” of simulation involves probability and statistics, thus a good background in probability and statistics is a required prerequisite
Course Outcomes:
1. Understand the meaning of simulation and its importance in business, science, engineering, industry and services
2. Identify the common applications of discrete-event system simulation.
3. Practice formulation and modeling skills.
4. Understand simulation languages
5. Ability to analyze events and inter-arrival time, arrival process, queuing strategies, resources and disposal of entities
6. An ability to perform a simulation using spreadsheets as well as simulation language/package
7. Ability to generate pseudorandom numbers using the Linear Congruential Method
8. Ability to perform statistical tests to measure the quality of a pseudorandom number generator
9. Ability to define random variate generators for finite random variables
10. Ability to analyze and fit the collected data to different distributions
