Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: Informatica
Programul de studii: Modelare si simulare - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MMC1015	Discrete Simulation	

Semester	Hours: C+S+L+P	Category	Status	
1	2+1+0+1	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
TRÎMBITAS Radu Tiberiu	Ph.D.	Assoc.Prof.	Applied Mathematics	*			

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
To introduce student to basic techniques of discrete-event simulation techniques, to learn them to develop discrete-event simulation programs

IV. Course contents
1.	Introduction to  Simulation and Discrete-Event Simulation (Banks, Ross, Law)
2.	Simulation Examples (Banks)
3.	General Principles (concepts in DES, Event Scheduling/Time Advance Algorithm, List Processing)
4.	Probability and Statistical Models (Banks, Ross, Law)
5.	 Probability and Statistical Models (Banks, Ross, Law)
6.	Random processes
7.	Queueing Models (Banks, Ross)
8.	Random Number Generation (Banks, Ross, Handbook)
9.	Random-Variate Generation (Banks, Ross, Handbook)
10.	Input Modelling
11.	Verification and Validation
12.	Output Data Analysis
13.	Variance Reduction Techniques and Monte-Carlo Simulation
14.	Projects presentation

V. Bibliography

1.	J. Banks, J. S. Carson II, Barry L. Nelson, David M. Nicol, Discrete-Event System Simulation, 4th edition, Prentice Hall, 2005
2.	Averill M. Law, Simulation Modeling and & Analysis, 4th edition, McGraw-Hill, 2007
3.	Sheldon Ross, Simulation, 4th edition, Elsevier 2006
4.	J. Banks (ed), Handbook of Simulation, Wiley and Sons, 1998

VI. Thematic of didactic activities per weeks
Week 1. Lecture: Introduction to Simulation, Seminar: Review of MATLAB
Week 2. Lecture: Simulation examples.
Week 3. Lecture: General principles of DES, Seminar: Simulation Examples in Excel
Week 4. Lecture: Review of Probability and Statistics
Week 5. Lecture: Review of Probability and Statistics. Seminar: Experiments with classical probability distribution in MATLAB Statistics Toolbox
Week 6. Lecture: Stochastic Processes.  Queueing Models
Week 7. Lecture: Queueing Models. Seminar: Applications  in MATLAB Statistics Toolbox
Week 8. Lecture: Random Number Generation.
Week 9. Lecture: Random Variate Generation. Seminar: Applications to Random Number and Random Variate Generation
Week 10. Lecture: Input Modelling. 
Week 11. Lecture: Verification and Validation. Seminar: Introduction to Simulink and SimEvents	

Week 12. Lecture: Output Data Analysis. 
Week 13. Lecture: Variance Reduction Techniques and Monte-Carlo Simulation. Laboratory: Applications in Simulink and SimEvents.
Week 14. Project presentation.	
VII. Didactic methods used

Exposition, Conversations, Use of problems, Discovery, Individual study, Exercise, Homework study, Computer demonstration, Usage of mathematical software.	

VIII. Assessment
Exam 40%, Project 40%, semester activity 20%	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
							Assoc.Prof. TRÎMBITAS Radu Tiberiu, Ph.D.