Academic year 2019/2020
- Course ID
- Prof. Gianfranco Balbo
- 2nd year
- Teaching period
- First semester
- D.M. 270 TAF C - Related or integrative
- Course disciplinary sector (SSD)
- INF/01 - informatica
- Formal authority
- Type of examination
- The basis of Probability Theory and Elements of Statistics are assumed to be known by the students.
The knowledge of a general purpose programming language is required in order to implement the simulators required as part of the homework exercises and of the final project.
Sommario del corso
Simulation is one of the most common techniques used for the evaluation of the performance and if the reliability of Discrete Event Dynamic Systems (DEDS) often modelled with Stochastic Processes. Discrete Event Simulation consists on the execution of a program which results in the production of a realization of a stochastic process driven by Monte-Carlo methods. Learning how to construct a simulator is the main objective of this course, together with the development of the techniques needed for the statistical analysis of the simulation output. To deeply understand the difficulty of writing an efficient simulator equipped with the output analysis components, students will be required to write a few simple simulators “from scratch” without using available tools and libraries.
Results of learning outcomes
At the end of the course the students will be able to perform the simulation of non-trivial Discrete Event Sysrtems. The exercises and the final project will provide the students with the capability of writing the simulators using a general purpose programming language of their choice. Having developed the simulators “fromn scratch” will allow the students to understand the potentials and the limits of the Discrete Event Simultauion technique, thus providing them with the capability of using professional simulators with competence
The course will be based on theretical lessons as well as on the soltion of class exercises. Computer implementations will be required as homework assignments. Personal training on assigned exercises is important for the success in this class.
Learning assessment methods
The final examination will consist in the discussion of a project developed individually by the students used as the basis for asking questions on the theoretical aspects of the exercise. Students will not be required to be able to reproduce the derivations used to obtain the results discussed during the course, but will have to know the definitions and the applications of the theory.
The final grade will be out of thirty.
Exercises will be assigned as homework. The course will include sessions devoted to the discussion of the solutions of selected homework, as well as to the solution of additional exercises.
- Discrete Event Dynamic Systems nodelling and performance indices
- Formalisms for System Modelling
- Introduction, measurable entities and operational variables
- Flow analysis in queuing networks
- Balance equations
- Queuing networks with product form solution
- Computational algorithms for product form solution
- Introduction to Discrete Event Simulation
- Construction of a simple simulator
- Random number generators
- Generating instances of random variables
- Data structures and basic architecture of a simulator
- Statistical analysis of simulation output
Suggested readings and bibliography
-Lawrence M. Leemis and Stephen K. Park, "Discrete-Event Simulation: a first course", Pearson Prentice Hall, 2006.
-George S. Fishman, "Principles of Discrete Event Digital Simulation", John Wiley & Sons, 1978.
-Hisashi Kobayashi, "Modeling and Analysis: An Introduction to System Performance Evaluation Methodology", Addison-Wesley, 1978.
-Kishor S. Trivedi, "Probability and Statistics with Reliability, Queueing and Computer Science Applications", Prentice Hall, 1982.
-Giuseppe Iazeolla, "Introduzione alla Simulazione Discreta", Boringhieri.
-Additional Lecture Notes will be made available to the students.