Person in charge: | (-) |
Others: | (-) |
Credits | Dept. | Type | Requirements |
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7.5 (6.0 ECTS) | EIO |
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EST
- Prerequisite for DIE , DCSFW |
Person in charge: | (-) |
Others: | (-) |
By modelling them, students are introduced to the operations research techniques used in analysing systems for quantitative decision making in the presence of uncertainty.
Estimated time (hours):
T | P | L | Alt | Ext. L | Stu | A. time |
Theory | Problems | Laboratory | Other activities | External Laboratory | Study | Additional time |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
2,0 | 0 | 0 | 0 | 0 | 1,0 | 0 | 3,0 | |||
Basic elements in the methodology for constructing models of systems modelling systems under conditions of uncertainty.
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
4,0 | 4,0 | 0 | 0 | 0 | 8,0 | 0 | 16,0 | |||
a. Examples. b. Decision-making processes in analyzing systems based on queuing models c. Formulation of cost functions based on queuing models d. System analysis models. Applications of open networks (with and without feedback) and closed networks to the modelling of a central computer server. Extensions to the generalisation of service time and multiplicity of job types. |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
6,0 | 4,0 | 0 | 0 | 0 | 10,0 | 0 | 20,0 | |||
a. Statistical models concerning reliability. Graphic methods. b. Standard fault prediction and modelling methods: basic series, active redundancy. c. Availability models for reparable systems. Availability and maintenance cost: Modular design. Analysis, using block diagrams and Markovian processes. Simulation limitations and treatment. |
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T | P | L | Alt | Ext. L | Stu | A. time | Total | ||
---|---|---|---|---|---|---|---|---|---|---|
20,0 | 2,0 | 14,0 | 0 | 10,0 | 20,0 | 0 | 66,0 | |||
a. Introduction to system simulations: simulation event scheduling. b. Basic simulation models for queues, reliability, etc. c. Introduction to Monte Carlo methods: generation of random numbers and samples of probability distributions. d. Introduction to simulation experiments and analysis of simulation results. |
Total per kind | T | P | L | Alt | Ext. L | Stu | A. time | Total |
47,0 | 18,0 | 14,0 | 0 | 10,0 | 54,0 | 0 | 143,0 | |
Avaluation additional hours | 7,0 | |||||||
Total work hours for student | 150,0 |
The course will take a practical approach with regard to the application of models, especially in those cases where models provide the most efficient way of representing and analyzing computing systems (e.g. queues and queue networks).
The teaching approach adopted in the course combines theory classes, problems, and lab sessions. This is complemented by a small project in which students must build a model for analyzing and evaluating system behaviour.
Course assessment is based on the following:
1. A test (C1) will be held halfway through the term in class time.
2. A test (C2) will be held before the end of term and in class time.
3. Simulation course (C3). This will be carried out during the lab sessions and involve individual work at an external lab.
4. The final exam will comprise two parts directly bearing on the materials examined in tests C1 an C2, (following on from C4-1 and C4-2).
The Continuous Assessment grade and Final Exam grade will be determined as follows:
Continuous Assessment = 0.5*C1+0.5*C2
Final Exam = 0.5*Max(C1,C4-1)+0.5*Max(C2,C4-2)
Award of the Final Grade (NF) will require submissions of the course practical work (C3) and be calculated as follows:
NF = 0.8*Max(NAC,NEF)+0.2*C3
The maximum Final Grade is 4.
Students who pass both the two interim tests may sit the final exam if they so wish.
Algebra, Analysis, Statistics