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.
Simulation is 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 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 the proper collection and modeling of input data, inclusion of random phenomena in the model to emulate the behavior of the real system, 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. SIMIO), as well as its use in the study and resolution of practical cases in different fields of knowledge.

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This is a compulsory subject within the master's program and is closely related to the subjects of Operations Research, Metaheuristic Optimization, and the TFM in Modeling and Simulation.

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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 basic knowledge is required (bachelor degree level) in mathematics, statistics, and probability. 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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This course does not require having followed any other one within the master's degree program.

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The main goals of this course are:
  • To introduce students to 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.

Among the master's competencies, this course will allow you to acquire the following:
  • Understand and apply advanced computing knowledge and numerical or computational methods to engineering problems.
  • Apply computational, mathematical and statistical methods to model, design and develop applications, services, intelligent systems and knowledge-based systems.
  • Apply mathematical and computational methods to solve technological problems and company engineering problems, particularly in research, development and innovation tasks.
  • Ability to model problems using the language of mathematics and solve them with formal reasoning.
  • Identify the mathematical theories needed to construct models based on problems from other disciplines.
  • Handle mathematics and statistics software.
  • Model, simulate and analyze systems, processes and networks.

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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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Simulation PDF
Simulación con Simio PDF
Simulation with Simio PDF

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Assessment at the UOC is, in general, online, structured around the continuous assessment activities, the final assessment tests and exams, and the programme's final project.

Assessment activities and tests can be written texts and/or video recordings, use random questions, and synchronous or asynchronous oral tests, etc., as decided by each teaching team. The final project marks the end of the learning process and consists of an original and tutored piece of work to demonstrate that students have acquired the competencies worked on during the programme.

To verify students' identity and authorship in the assessment tests, the UOC reserves the right to use identity recognition and plagiarism detection systems. For these purposes, the UOC may make video recordings or use supervision methods or techniques while students carry out any of their academic activities.

The UOC may also require students to use electronic devices (microphones, webcams or other tools) or specific software during assessments. It is the student's responsibility to ensure that these devices work properly.

The assessment process is based on students' individual efforts, and the assumption that the student is the author of the work submitted for academic activities and that this work is original. The UOC's website on academic integrity and plagiarism has more information on this.

Submitting work that is not one's own or not original for assessment tests; copying or plagiarism; impersonation; accepting or obtaining any assignments, whether for compensation or otherwise; collaboration, cover-up or encouragement to copy; and using materials, software or devices not authorized in the course plan or instructions for the activity, including artificial intelligence and machine translation, among others, are examples of misconduct in assessments that may have serious academic and disciplinary consequences.

If students are found to be engaging in any such misconduct, they may receive a Fail (D/0) for the graded activities in the course plan (including final tests) or for the final grade for the course. This could be because they have used unauthorized materials, software or devices (such as artificial intelligence when it is not permitted, social media or internet search engines) during the tests; copied fragments of text from an external source (the internet, notes, books, articles, other students' work or tests, etc.) without the corresponding citation; purchased or sold assignments, or undertaken any other form of misconduct.

Likewise and in accordance with the UOC's academic regulations, misconduct during assessment may also be grounds for disciplinary proceedings and, where appropriate, the corresponding disciplinary measures, as established in the regulations governing the UOC community (Normativa de convivència).

In its assessment process, the UOC reserves the right to:

  • Ask students to provide proof of their identity as established in the UOC's academic regulations.
  • Ask students to prove the authorship of their work throughout the assessment process, in both continuous and final assessments, through a synchronous oral interview, of which a video recording or any other type of recording established by the UOC may be made. These methods seek to ensure verification of the student's identity, and their knowledge and competencies. If it is not possible to ensure the student's authorship, they may receive a D grade in the case of continuous assessment or a Fail grade in the case of the final assessment.

Artificial intelligence in assessments

The UOC understands the value and potential of artificial intelligence (AI) in education, but it also understands the risks involved if it is not used ethically, critically and responsibly. So, in each assessment activity, students will be told which AI tools and resources can be used and under what conditions. In turn, students must agree to follow the guidelines set by the UOC when it comes to completing the assessment activities and citing the tools used. Specifically, they must identify any texts or images generated by AI systems and they must not present them as their own work.

In terms of using AI, or not, to complete an activity, the instructions for assessment activities indicate the restrictions on the use of these tools. Bear in mind that using them inappropriately, such as using them in activities where they are not allowed or not citing them in activities where they are, may be considered misconduct. If in doubt, we recommend getting in touch with the course instructor and asking them before you submit your work.

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You can only pass the course if you participate in and pass the continuous assessment. Your final mark for the course will be the mark you received in the continuous assessment.

 

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