Code: | L.EEC021 | Acronym: | IO |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Management |
Active? | Yes |
Web Page: | https://sites.google.com/g.uporto.pt/io-or |
Responsible unit: | Department of Industrial Engineering and Management |
Course/CS Responsible: | Bachelor in Electrical and Computer Engineering |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
L.EEC | 256 | Syllabus | 3 | - | 6 | 52 | 162 |
M.EEC | 83 | Syllabus | 1 | - | 6 | 52 | 162 |
BACKGROUND
This course focuses on the application of analytical methods to make better decisions and provides students with tools for modelling and optimization that will be very useful in various roles in several types of organizations (industry and services).
SPECIFIC OBJECTIVES
The main objective of this course is, through the creation of models, to develop skills for analysing a wide range of real situations. These competencies are based on the ability to recognize the key problem in a non-structured situation, on the ability to develop a framework for analysing and treating the problem and on the application of analytical methods for its resolution.
LEARNING OBJECTIVES
Endow the students with the skills to:
This course will contribute to the acquisition, by the students, of the following CDIO competencies:
1. Technical Knowledge and Reasoning
1.1. Acquire proficiency with the necessary knowledge of basic sciences and be able to use them in the formulation, discussion and resolution of problems of their area;
The contents of the course unit build on the knowledge of Algebra, Mathematical Analysis and Probabilities and Statistics, organizing them, developing them and applying them in a context of decision support. By nature, this knowledge is transversal to all areas of Engineering and belongs to its basic sciences. Throughout the classes there is a concern to use examples and case studies from the scientific area of each course, bridging the gap between the basic sciences and the area of the degree.
The assessment in the course addresses these skills.
2. Personal and Professional Skills and Attitudes
2.1. Engineering reasoning and problem-solving
Exactly these points can describe the teaching methodology and the contents of this course. Thus, the formulation of problems according to each of the models studied throughout the course, their modelling and resolution are an ongoing activity. The qualitative analysis is addressed in the sensitivity analysis, while uncertainty is addressed in Queueing Theory and in Decision Theory.
The assessment in the course addresses these skills.
2.2 Experimentation and knowledge discovery
2.3 systemic thinking
These skills are addressed indirectly and maybe more directly described as:
Problem design and modelling, involving the identification of the applicable family of models and the development and evaluation of original models, clearly contributes to these skills. The topics related to sensitivity analysis contribute to the understanding of concepts related to trade-offs and balancing.
The assessment in the course addresses these skills.
The learning objectives are related to the competencies (knowledge, skills and attitudes that students should have) and are expressed as statements about things that students should be able to explain, calculate, deduct, design, etc ...
For all program topics, detailed learning objectives are available from the website, along with educational materials to support learning.
Basic courses in algebra, statistics and probability theory.
The themes are presented using active learning methods. The problems are illustrated with examples. The problems from a list of proposed problems are discussed and solved by the students.
Designation | Weight (%) |
---|---|
Exame | 30,00 |
Teste | 70,00 |
Total: | 100,00 |
Designation | Time (hours) |
---|---|
Estudo autónomo | 110,00 |
Frequência das aulas | 52,00 |
Total: | 162,00 |
DISTRIBUTED EVALUATION
Micro-exercises in class (closed book) (0 to 10 points)
The sum of the scores obtained by each student in the assessment exercises proposed for resolution at the end of each lesson, removing the two lowest scores.
Each exercise will be rated on a scale from 0 to 100%.
Test (closed book) (0 to 4 points).
Distributed evaluation: final rate between 0 and 14 points.
EXAM (open book) (0 to 6 points).
“Operational research is the discipline of applying advanced analytical methods to help make better decisions. By using techniques such as problem structuring methods […] and mathematical modeling to analyze complex situations, operational research gives executives the power to make more effective decisions and build more productive systems”.
Demonstration of the coherence between the teaching methodologies and learning outcomes
The active learning principles are already well established in Pedagogy, as leading the student to better and longer duration learning outcomes, once it fully involves the student in its own learning process. Therefore, all activities developed in class are meant to implement the active learning principles and, at the same time, to immediately consolidate (by doing) the concepts just learned from the teacher lecture.
The practical component of the classes is organized according to the principles of cooperative learning. Students are organized in groups of 4 and try to solve, during the class, the problems proposed by the teacher, in a collaborative way. The teacher supports each group in the difficulties that are not overcome in the group’s internal discussion, acting as a facilitator of the student's learning process and not as the centre of the class. The practice classes are supposed to work as a consolidation of the learning process started in the lecture class and in the student’s self-study.
This consolidation is even further reinforced by the individual assessment that weekly is run. At the end of the class, a small exercise has to be solved (closed book) by the students, which is marked and discussed with them in the following week, reinforcing the formative component of the assessment. These exercises address the lower levels of the cognitive domain of the taxonomy of Bloom.
The teaching and assessment methodologies are, therefore, coherent with the curricular unit’s intended learning outcomes.