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 second semester of the academic year 2023/2024. 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
SimulaciĆ³n con Simio PDF
Simulation with Simio PDF

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The assessment process is based on students' own work and the assumption that this work is original and has been carried out by them.

In assessment activities, the following irregular behaviours, among others, may have serious academic and disciplinary consequences: someone else being involved in carrying out the student's assessment test or activity, or the work being not entirely original; copying another's work or committing plagiarism; attempting to cheat to obtain better academic results; collaborating in, covering up or encouraging copying; or using unauthorized material, software or devices during assessment.

If students are caught engaging in any of these irregular behaviours, they may receive a fail mark (D/0) for the assessable activities set out in the course plan (including the final tests) or in the final mark for the course. This could be because they have used unauthorized materials, software or devices (e.g. social networking sites or internet search engines) during the tests, because they have copied text fragments from an external source (internet, notes, books, articles, other student's projects or activities, etc.) without correctly citing the source, or because they have engaged in any other irregular conduct.

In accordance with the UOC's academic regulations , irregular conduct during assessment, besides leading to a failing mark for the course, may be grounds for disciplinary proceedings and, where appropriate, the corresponding punishment, as established in the UOC's coexistence regulations.

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

  • Ask the student to provide proof of their identity, as established in the university's academic regulations.
  • Request that students provide evidence of the authorship of their work, throughout the assessment process, both in continuous and final assessment, by means of an oral test or by whatever other synchronous or asynchronous means the UOC specifies. These means will check students' knowledge and competencies to verify authorship of their work, and under no circumstances will they constitute a second assessment. If it is not possible to guarantee the student's authorship, they will receive a D grade in the case of continuous assessment or a Fail in the case of final assessment.

    For this purpose, the UOC may require that students use a microphone, webcam or other devices during the assessment process, in which case it will be the student's responsibility to check that such devices are working correctly.

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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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