Course: 2019/2020

Computer modeling and simulation methods

(14350)

Competences:
Ability to mathematically model, compute and simulalte in CS subjects.
Ability to apply acquired knowledge and to solve problems in uncertain environments, integrating new knowledge
Ability to express conclusions, knowledge and motivations in a clear and unambiguitous way.
Ability to analyze information requirements of a given domain, and carry out the development of the corresponding information system.
Skills:
Resultados de aprendizaje:
Skill of implementing computer simulation models applied to engineering problems.
Skill of analyzing computer modeling and simulation methods.
Skill of developing open practicework with enough critical thinking.
Skill of converging acquired knowledge and its application to problems
Skill of integrating multidisciplinary knowledge.

Description of contents: programme

1. Introduction
11 Introduction
12 Obtaining models
13 Application domains
14 Types of simulation models.
15 Simulation steps.
16 Pros and cons.
17 Frequent mistakes in simulation.
2. Random Numbers
21 Definition and types.
22 Motivation.
23 History
24 Properties
25 Types of generators.
26 Conclusions.
3. Generation of randomo distributions
31 General concepts.
32 General methods.
33 Specific methods.
34 Conclusions.
4. Discrete event simulation.
41 Introduction.
42 Simulation of discrete events.
43 Modeling tools
44 Conclusions.
5. Distributed Simulation
51 Introduction
52 Parallel archistectures
53 Sincronization.
54 Conclusions
6. Monte Carlo
61 Introduction.
62 Motivation.
63 History
64 Monte Carlo: Steps and examples.
65 Monte Carlo in Excel.
66 Conclusions.
7. Análysis of simulation results.
71 Introduction.
72 Average and deviations.
73 Positioning measures.
74 Boxplot graphics.
75 Confidence intervals.
76 Contrasting Hypotheses.
TEMA 8. Modeling and simulating complex systems: Traffic simulation.
81 Introduction
82 Generating the network.
83 Generating the traffic,
84 Simulations
85 Analysis of results.
Analysis with R
91 Introduction to R.
92 Accessing data in xml files with R
93 Commonly used statistics.
94 Contrasting hypotheses.
95 Processing results.
96 Tests in R.

Learning activities and methodology

- Theoretical lectures
- Practical works individually or in team
- Exercises of problem solving.
- Personal Homework

Assessment System

- % end-of-term-examination 40
- % of continuous assessment (assigments, laboratory, practicals...) 60

Basic Bibliography

- A.M. Law; W.D. Kelton. SIMULATION MODELLING AND ANALYSIS. McGraw-Hill . 1991
- David, Nuno; Sichman, Jaime Simao. Multi-Agent-Based Simulation IX. Springer. 2009
- J. Banks; J.S. Carson; B.L. Nelson. . DISCRETE EVENT SYSTEM SIMULATION. Prentice Hall. 1996
- Jerry Banks. Handbook of simulation : principles, methodology, advances, applications and practice. Jerry Banks.
- John A. Sokolowski, Catherine M. Banks . Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains. John Wiley & Sons, Inc. 2010
- Levent Yilmaz, Tuncer Ören. Agent-Directed Simulation and Systems Engineering. Wiley. 2009
- Michael J. North, Charles M. Macal. Managing Business Complexity. Oxford University Press. 2007
- Phan, Denis, and Amblard, Frédéric. Agent-Based Modelling and Simulation. The Bardwell Press. 2007
- Uhrmacher, Adelinde.. Multi-agent systems : simulation and applications. Taylor & Francis. 2009.

Additional Bibliography

- . Banks, J. S. Carson, B.L. Nelson, D.M. Nicol, Pearson, J.. Discrete-Event Systems Simulation,. Prentice Hall.
- B. S. Bennet,. Simulation Fundamental,. Prentice-Hall.
- Edited by Jerry Banks. Handbook of simulation : principles, methodology, advances, applications and practice /. John Wiley & Sons,.
- F. Cellier, E. Kofman,. Continuous systems simulation.. Springer.
- R. L. Woods, K. L. Lawrence,. Modeling and Simulation of Dynamic Systems,. Prentice-Hall.