Computer science | SIMULATION
Computer science SIMULATION
|DIPARTIMENTO DI INFORMATICA|
|YEAR OF COURSE 3|
|YEAR OF DIDACTIC SYSTEM 2015|
|KNOWLEDGE AND UNDERSTANDING|
•BASIC KNOWLEDGES NEEDED TO PROBABILISTIC DESCRIPTION OF THE MOST SIMPLE QUEUEING SYSTEMS WITH ONE OR MORE SERVERS, AND ANALYSIS OF THEIR PERFORMANCE MEASURES AND OF RELIABILITY
•SIMULATION MODELS, PROBLEM OF CHOICE OF SIMULATOR AND PLANNING OF AN SIMULATION EXPERIMENT.
APPLYING KNOWLEDGE AND UNDERSTANDING
•BUILDING OF PSEUDO-RANDOM SEQUENCES, UNIFORM AND NON-UNIFORM, BY APPLYING APPROPRIATE STATISTICAL METHODS TO GENERATED SEQUENCES.
•SIMULATION OF SIMPLE QUEUEING SYSTEMS, WITH THE PURPOSE OF OBTAINING ESTIMATES OF THE PERFORMANCE MEASURES OF THE SYSTEM.
|Basic knowledge of probability and statistics.|
|•QUEUEING SYSTEMS: INTRODUCTION TO SERVICE SYSTEMS. SOURCE, WAITING CENTER, SERVICE CENTER, DESTINATION. SERVICE DISCIPLINE. MECHANISM OF ARRIVALS AND SERVICE MECHANISM. INTERARRIVAL AND SERVICE TIMES: DETERMINISTIC, UNIFORM, EXPONENTIAL, ERLANG, HYPEREXPONENTIAL. KENDALL'S NOTATION IN THE THEORY OF QUEUES|
•ANALYSIS OF THE SYSTEM: SOME PERFORMANCE MEASURES. STATE OF THE SYSTEM. WAITING TIME IN THE SYSTEM AND IN THE QUEUE. TRAFFIC INTENSITY AND UTILIZATION FACTOR OF THE SYSTEM. LITTLE’S LAW. IDLE AND BUSY PERIODS
•BIRTH DEATH PROCESSES: POISSON PROCESS. BIRTH-DEATH STOCHASTIC PROCESSES. STATISTICAL EQUILIBRIUM. PRINCIPLE OF BALANCE
•MODELS WITH A SINGLE SERVER: SERVICE SYSTEMS M/M/1, M/M/1/K, M/G/1
•MODELS WITH MORE SERVES: SERVICE SYSTEM M/M/2. COMPARISON BETWEEN SYSTEMS M/M/1 AND M/M/2. SERVICE SYSTEMS M/M/S, M/M/S/S AND M/M/. SYSTEMS WITH SERVICE ACCELERATION AND WITH DISCOURAGEMENT
•SIMULATION: INTRODUCTION TO SIMULATION. CLASSIFICATION OF SIMULATORS. MONTE CARLO METHOD AND ITS APPLICATIONS. SIMULATION OF A SERVICE SYSTEM WITH ONE AND TWO SERVERS.
•GENERATORS UNIFORMS: INTRODUCTION TO THE GENERATION OF PSEUDORANDOM SEQUENCES. METHOD OF THE CENTER OF THE SQUARE. METHOD MULTIPLICATIVE CONGRUENTIAL. OTHER TYPES OF CONGRUENT GENERATORS. UNIFORM GENERATORS IN (0.1)
•DISCRETE AND CONTINUOUS GENERATORS: METHODS FOR THE GENERATION OF CONTINUOUS RANDOM VARIABLES: INVERSION METHOD OF THE DISTRIBUTION FUNCTION AND METHOD OF REJECTION. GENERATION OF SOME CONTINUOUS RANDOM VARIABLES: EXPONENTIAL, NORMAL AND OF ERLANG. METHOD COMPOUND. GENERATION OF A HYPEREXPONENTIAL RANDOM VARIABLE. METHODS FOR THE GENERATION OF DISCRETE RANDOM VARIABLES. GENERATION OF SOME DISCRETE RANDOM VARIABLES: GEOMETRIC, BINOMIAL AND OF POISSON. SIMULATION WITH R.
|The teaching method includes theoretical lessons, integrated by examples and problems, related to the performance and of the reliability of queueing systems (CFU 6, length (h): 48). The class attendance is strongly recommended. Students are guided to learn, in a critical and responsible way, everything what the teacher presents during the lectures. Students are thus encouraged to communicate to the entire class the ideas of development and of problem solving, and are also encouraged to acquire skills and expertise in managing the complexity of new problems concerning to the performances of queueing systems.|
|Verification of learning|
|The final discussion consists of an oral examination. The vote will depend on the knowledge and understanding of the basic concepts and on the ability to make the necessary connections between the various topics addressed in the lessons.|
|•Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol (2013) Discrete-Event System Simulation. Pearson Education International|
•Sheldon M. Ross (2013) Simulation. Academic Press
•Lecture Notes of The Teacher
|To help students in individual study, the teacher will provide notes, inclusive of the topics considered and of the relevant problems. Students who have attended assiduously have an advantage in the oral discussion because they have been stimulated to learn and to connect various topics systematically and critically.|
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2019-05-14]