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.	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.	Random processes (Ross, Law)
6.	Queueing Models (Banks, Ross)
7.	Random Number Generation (Banks, Ross, Handbook)
8.	Random-Variate Generation (Banks, Ross, Handbook)
9.	Random-Variate Generation – stochastic processes and multivariate
10.	Input Modelling (Banks, Law)
11.	Verification and Validation (Banks, Law)
12.	Output Data Analysis – single system (Banks, Law)
13.	Output Data Analysis – alternative design (Banks, Law)
14.	Variance Reduction Techniques and Monte-Carlo Simulation

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

VI. Thematic of didactic activities per weeks
Week 1. Lecture: Introduction to Simulation
Week 2. Lecture: Simulation examples , Seminar: Simulation Examples in Excel
Week 3. Lecture: General principles of DES
Week 4. Lecture: Review of Probability and Statistics, Seminar: Experiments with classical probability distribution in MATLAB Statistics Toolbox
Week 5. Lecture: Stochastic Processes.  
Week 6. Lecture: Queueing Models. Seminar: Introduction to Simulink and SimEvents
Week 7. Lecture: Random Number Generation.
Week 8. Lecture: Random Variate Generation. Seminar: Applications to Random Number Generation
Week 9. Lecture: Random-Variate Generation – stochastic processes and multivariate RVG 
Week 10. Lecture: Input Modeling. Seminar: Applications to Input DATA Analysis
Week 11. Lecture: Verification and Validation. 
Week 12. Lecture: Output Data Analysis. Applications to Verification and Validation.
Week 13. Lecture: Output Data Analysis – alternative design
Week 14: Lecture: Variance Reduction Techniques and Monte-Carlo Simulation	

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%, Practical exam 40%, semester activity 20%	

IX. Additional references

	Dean,							Chair head,

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