Simulación Código:  M0.500    :  6
Consulta de los datos generales   Descripción   Información previa a la matrícula   Objetivos y competencias   Contenidos   Consulta de los materiales de los que dispone la asignatura  
ATENCIÓN: Esta información recoge los apartados del plan docente de la asignatura durante el último semestre con docencia. Al iniciar el periodo de matrícula, podrás consultar el calendario y modelo de evaluación para el siguiente semestre en Trámites / Matrícula / Horarios de las pruebas de evaluación final.
Welcome to this graduate course on Discrete-Event Simulation, a hybrid discipline that combines knowledge and techniques from Operations Research (OR) and Computer Science (CS). Due to the fast and continuous improvements in computer hardware and software, Simulationhas become an emergent research area with practical industrial and services applications.  Today, most real-world systemsare too complex to be modeled and studied by using analytical methods.  Instead, numerical methods such as simulation must be employed in order to study the performance of those systems, to gain insight into their internal behavior and to consideralternative ("what-if") scenarios.  Applications of Simulations are widely spread among different knowledge areas, including the performance analysis of computer and telecommunication systems or the optimization ofmanufacturing and logistics processes.  This course introduces concepts and methods for designing, performing and analyzing experiments conducted using aSimulation approach.  Among other concepts, this course discusses the proper collection and modeling of input data and system randomness, the generation of random variables to emulate the behavior of the real system, the verification and validation of models, and the analysis of the experimental outputs.

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  • Descripción: Este curso permite al estudiante conocer los conceptos y adquirir las habilidades necesarias para modelar y simular sistemas, redes y procesos mediante el uso de técnicas de Simulación Monte Carlo (MCS) y Simulación de Eventos Discretos (DES). Para ello, el curso incluye el aprendizaje teórico-práctico de métodos de modelado de datos asociados a fenómenos aleatorios, generación de números pseudo-aleatorios, diseño de algoritmos de simulación, diseño de experimentos, verificación y validación, análisis de resultados, y comparación de diseños alternativos. El curso también incluye el aprendizaje de software específico para modelado y simulación (e.g. Simio, etc.), así como su uso en el estudio y resolución de casos prácticos en diferentes ámbitos de conocimiento. 
  • Requisitos: Capacidad para leer textos científicos en inglés. Conocimientos básicos de estadística (nivel licenciatura o ingeniería).
  • Bibliografía prevista: Robinson, S. (2004). Simulation: The Practice of Model Development and Use. Wiley.
  • Software previsto: SIMIO Simulation Software (http://www.simio.com)
  • Enlaces: WSC Archive (http://informs-sim.org

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The main goals of this course are:

  • To introduce students in the prolific research field of discrete-event simulation. In particular, this course offers a set of powerful system modeling and analysis tools (concepts, techniques and skills) that students can use both in their research and professional careers.
  • To develop students' modeling, analytical-thinking and synthesis skills.  In particular, throughout the course students will have to: model systems or processes in order to analyze them, read scientific papers, and develop their own simulation skills.

Course objectives are derived from the course goals and designed to be assessable. By the end of this course, students should be able to:

  • Apply scientific thinking to the analysis ofcomplex systems and processes.
  • Comprehend important concepts in computer modeling and simulation.
  • Model uncertainty and randomness by means of statistical distributions.
  • Form a hypothesis and design acomputer experiment to test it.
  • Collect and model data, estimate errors in the results and analyze simulation outputs.
  • Understand how computers generate (pseudo-)random numbers and variables.
  • Realize the application scope and limitations of computer simulation techniques.
  • Employ statistical techniques to construct scientific statements and conclusions.
  • Construct, verify and validate system and processes models.
  • Understand the main ideas described in scientific papers on simulation.

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1. Simulation: What, Why and When? 2. Inside Simulation Software 3. Software for Simulation 4. Simulation Studies 5. Conceptual Modeling 6. Developing the Conceptual Model 7. Data Collection and Analysis 8. Model Coding 9. Obtaining Accurate Results 10. Searching the Solution Space 11. Implementation 12. Verification & Validation 13. The practice of simulation

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Simulación PDF
MATLAB_ENG PDF
MATLAB PDF
Simulation PDF

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