Operations Research Code:  M0.531    :  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   Additional information on support tools and learning resources   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.
Operations Research (OR) is a discipline that relies on the formulation of mathematical-computational models and the development of algorithms for solving problems linked to efficient decision-making in any field and sector (business, industrial, social, health, services, etc.).

This course provides the OR concepts necessary to model and solve real problems using techniques such as linear programming, integer programming, nonlinear programming, etc. In particular, the course will focus on practical applications of OR concepts and techniques, in order to solve problems related to the fields of logistics and transportation, system and networks' optimization, and process scheduling.

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This course is strongly related to other courses of the master like Simulation, Metaheuristic Optimization, and the FMP in Modeling and Simulation.

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An excellent introductory video to Operations Research / Operations Management can be found at: https://www.youtube.com/watch?time_continue=1&v=sFWrmpXPVJw

It is also recommended to consult the following websites:
  • INFORMS: https://www.informs.org
  • SEIO: https://www.seio.es/
  • EURO: www.euro-online.org

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The following knowledge is assumed:
  • Ability to read scientific texts in English.
  • Basic knowledge of mathematics (bachelor or engineering's degree level).
  • Analytical capacity.
  • Interest in optimization applications in different areas (transport, logistics, finance, communication networks, etc.)

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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 objectives of the course are:
  • Develop skills for modeling optimization problems.
  • Improve analytical and problem-solving capabilities.
  • Acquire practical knowledge about the main optimization techniques.
  • Apply OR in areas such as logistics, transportation, finance, communications networks, etc.
  • Improve the ability to understand scientific texts in English.
  • Increase the capacity for synthesis and logical-mathematical reasoning.
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. Applications of Operations Research
  2. Intro to Optimization Modeling and Software
  3. Linear Programming Modeling
  4. Sensitivity Analysis and Duality
  5. Integer Programming Modeling
  6. Network Modeling and Optimization
  7. Nonlinear Optimization
  8. Multi-Objective Optimization
  9. Heuristics I
  10. Heuristics II
  11. Monte Carlo Simulation
  12. Discrete Event Simulation
  13. Overview of x-Heuristics

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In the course, in addition to the main book, the following software might be employed:
  • Excel Solver and Open Solver for Excel (advanced version): https://opensolver.org
  • Lindo / Lingo (https://www.lindo.com) or an equivalent software.

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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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