
Simulation	Code: M0.500 : 6

View general information Description The subject within the syllabus as a whole Professional fields to which it applies Prior knowledge Information prior to enrolment Learning objectives and results Content View the UOC learning resources used in the subject Guidelines on assessment at the UOC View the assessment model

This is the course plan for the first semester of the academic year 2024/2025. To check whether the course is being run this semester, go to the Virtual Campus section More UOC / The University / Programmes of study section on Campus. Once teaching starts, you'll be able to find it in the classroom. The course plan may be subject to change.

Description

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.

The subject within the syllabus as a whole

This is a compulsory subject within the master's program.

Professional fields to which it applies

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

Prior knowledge

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.

Information prior to enrolment

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)

Learning objectives and results

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.

Content

Simulation: What, Why and When?
Inside Simulation Software
Software for Simulation
Simulation Studies
Conceptual Modeling
Developing the Conceptual Model
Data Collection and Analysis
Model Coding
Obtaining Accurate Results
Searching the Solution Space
Implementation
Verification & Validation
The practice of simulation

View the UOC learning resources used in the subject


Simulation	PDF
Simulación con Simio	PDF
Simulation with Simio	PDF

Guidelines on assessment at the UOC

The assessment process is based on the student's personal work and presupposes authenticity of authorship and originality of the exercises completed.

Lack of authenticity of authorship or originality of assessment tests, copying or plagiarism, the fraudulent attempt to obtain a better academic result, collusion to copy or concealing or abetting copying, use of unauthorized material or devices during assessment, inter alia, are offences that may lead to serious academic or other sanctions.

Firstly, you will fail the course (D/0) if you commit any of these offences when completing activities defined as assessable in the course plan, including the final tests. Offences considered to be misconduct include, among others, the use of unauthorized material or devices during the tests, such as social media or internet search engines, or the copying of text from external sources (internet, class notes, books, articles, other students' essays or tests, etc.) without including the corresponding reference.

And secondly, the UOC's academic regulations state that any misconduct during assessment, in addition to leading to the student failing the course, may also lead to disciplinary procedures and sanctions.

The UOC reserves the right to request that students identify themselves and/or provide evidence of the authorship of their work, throughout the assessment process, and by the means the UOC specifies (synchronous or asynchronous). For this purpose, the UOC may require students to use a microphone, webcam or other devices during the assessment process, and to make sure that they are working correctly.

The checking of students' knowledge to verify authorship of their work will under no circumstances constitute a second assessment.

View the assessment model

This course can only be passed through continuous assessment (CA), the mark for which is combined with a practical (Pr) mark to give the final course mark. It is not planned to have any final exam or on-site validation test.The formula for accrediting the course is as follows: CA + Pr.The final course marks will be calculated as follows:
- If a pass mark is obtained for both CA and Pr, the final mark is given by the formula.
- If a pass mark is not obtained for CA, the final mark is the CA mark.
- If the CA has not been submitted, the final mark is Absent.
- If a pass mark is obtained for CA but not for Pr, the final mark is the Pr mark.
- If a pass mark is obtained for CA but the Pr mark is Absent, the final mark is Fail (2.5).