Simulació Codi:  M0.500    :  6
Consulta de les dades generals   Descripció   L'assignatura en el conjunt del pla d'estudis   Camps professionals en què es projecta   Coneixements previs   Informació prèvia a la matrícula   Objectius i competències   Continguts   Consulta dels recursos d'aprenentatge de què disposa l'assignatura   Informacions sobre l'avaluació a la UOC   Consulta del model d'avaluació  
Aquest és el pla docent de l'assignatura per al segon semestre del curs 2023-2024. Podeu consultar si l'assignatura s'ofereix aquest semestre a l'espai del campus Més UOC / La universitat / Plans d'estudis). Un cop comenci la docència, heu de consultar-lo a l'aula. El pla docent pot estar subjecte a canvis.

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, Simulation has become an emergent research area with practical industrial and services applications. Today, most real-world systems are 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 consider alternative ("what-if") scenarios. Applications of Simulations are widely spread among different knowledge areas, including the performance analysis of computer and telecommunication systems, the optimization of manufacturing and logistics processes or the analysis of environmental and social systems. This course introduces concepts and methods for designing, performing and analyzing experiments conducted using a Simulation 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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This is a compulsory subject within the master's program.

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The aim of this course is to train students to develop their professional careers within the following fields:
* Research
* Computer Simulation
* Artificial intelligence
* Operation Research
* Any related field

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This course is designed for postgraduate students in any of the following degrees: Computer Science, Telecommunications Engineering, Business Administration, Industrial Engineering, Economics, Mathematics or Physics. 

Some mathematical knowledge is required. It is assumed that the student has successfully completed at least three undergraduate courses in mathematics, one of them in probability and statistics. In addition, some programming knowledge is desirable but not essential. Finally, the student must be able to read the technical documentation written in English.

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Description: This course allows the student to know the concepts and acquire the necessary skills to model and simulate systems, networks and processes through the use of Monte Carlo Simulation (MCS) and Discrete Event Simulation (DES) techniques. For this, the course includes theoretical-practical learning of data modeling methods associated with random phenomena, generation of pseudo-random numbers, design of simulation algorithms, design of experiments, verification and validation, analysis of results, and comparison of alternative designs. The course also includes the learning of specific software for modeling and simulation (e.g. Ape, etc.), as well as its use in the study and resolution of practical cases in different fields of knowledge.

Requirements: Ability to read scientific texts in English. Basic knowledge of statistics (undergraduate or engineering level).

Planned bibliography: Robinson, S. (2004). Simulation: The Practice of Model Development and Use. Wiley.

Planned software: SIMIO Simulation Software (http://www.simio.com)

Links: 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 of complex 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 a computer 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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The learning units are the following:

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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Simulation PDF
Simulación con Simio PDF
Simulation with Simio PDF

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El procés d'avaluació es fonamenta en el treball personal de l'estudiant i pressuposa l'autenticitat de l'autoria i l'originalitat dels exercicis realitzats.

La manca d'autenticitat en l'autoria o d'originalitat de les proves d'avaluació; la còpia o el plagi; l'intent fraudulent d'obtenir un resultat acadèmic millor; la col·laboració, l'encobriment o l'afavoriment de la còpia, o la utilització de material o dispositius no autoritzats durant l'avaluació, entre d'altres, són conductes irregulars que poden tenir conseqüències acadèmiques i disciplinàries greus.

D'una banda, si es detecta alguna d'aquestes conductes irregulars, pot comportar el suspens (D/0) en les activitats avaluables que es defineixin en el pla docent –incloses les proves finals– o en la qualificació final de l'assignatura, sigui perquè s'han utilitzat materials o dispositius no autoritzats durant les proves, com ara xarxes socials o cercadors d'informació a internet, perquè s'han copiat fragments de text d'una font externa (internet, apunts, llibres, articles, treballs o proves d'altres estudiants, etc.) sense la citació corresponent, o perquè s'ha practicat qualsevol altra conducta irregular.

De l'altra, i d'acord amb les normatives acadèmiques, les conductes irregulars en l'avaluació, a més de comportar el suspens de l'assignatura, poden donar lloc a la incoació d'un procediment disciplinari i a l'aplicació, si escau, de la sanció que correspongui.

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Aquesta assignatura només es pot superar a partir de l'avaluació contínua (AC), nota que es combina amb una nota de pràctiques (Pr) per a obtenir la nota final de l'assignatura.La fórmula d'acreditació de l'assignatura és la següent: AC + Pr. Les qualificacions finals de l'assignatura es calcularan de la manera següent:
- Si l'AC i la Pr superen el mínim, la qualificació final serà el resultat de la fórmula de càlcul.
- Si no se supera el mínim de l'AC, la qualificació final serà la de l'AC.
- Si no es presenta l'AC, la qualificació final serà No presentat.
- Si l'AC supera el mínim i la Pr no, la qualificació final serà la nota de la Pr.
- Si l'AC supera el mínim i la Pr no es presenta, la qualificació final serà Suspès (2,5).

 
 

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