Checking date: 09/05/2018

Course: 2019/2020

Computer modeling and simulation methods
(14350)
Study: Master in Computer Engineering (228)
EPI

Coordinating teacher: CARBO RUBIERA, JAVIER IGNACIO

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:

Competences and skills that will be acquired and learning results.
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.