Operacional Research

Code:

M.BIO004

Acronym:

IO

Keywords
Classification	Keyword
OFICIAL	Economics

Instance: 2022/2023 - 1S

Active?	Yes
Web Page:	https://sites.google.com/g.uporto.pt/io-or
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Bioengineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.BIO	70	Syllabus	1	-	4,5	39	121,5
MEB	32	Syllabus	1	-	4,5	39	121,5

Teaching language

Portuguese

Objectives

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 todevelop 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:

- identify and address decision problems in a structured way;
- build models of decision problems;
- identify and use analytical methods to obtain solutions for the models, that should act as a support for informed decisions;
- use software packages to analyse and obtain solutions for the models;
- extract information from the models to communicate and motivate organizational changes.

Learning outcomes and competences

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
2.1.1 problem identification and formulation
2.1.2 modelling
2.1.3 estimation and qualitative analysis
2.1.4 analysis with uncertainty
2.1.5 solution and recommendation

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

2.2.1 hypothesizing
2.2.2 literature search
2.2.3 Experimental investigation
2.2.4 test and defence of hypotheses

2.3 systemic thinking

2.3.1 holistic thinking
2.3.2 emergency and interaction between systems
2.3.3 prioritization and focus
2.3.4 trade-offs, judgment and balancing the resolution

These skills are addressed indirectly and maybe more directly described as:

(1) analysis competencies - ability to conceptualize, formulate and solve unfamiliar problems;
(2) project competencies - ability to address with creativity, unfamiliar, complex and technically uncertain problems;
(3) research competencies - ability to model and evaluate the applicability of emerging techniques (description of the criteria according to Euro-ACE).

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.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Basic courses in algebra, statistics and probability theory.

Program

modelling | without integer variables
resolution of models | without integer variables





modelling | network problems
modelling | with integer variables
modelling | with integer auxiliary variables
resolution of problems with integer variables


queueing theory
decision theory

Mandatory literature

Docentes de IO | DEGI | FEUP; Documentação de apoio a Investigação Operacional (Available at the course site)

Complementary Bibliography

Hillier, Frederick S.; Introduction to operations research. ISBN: 0-07-118163-6
Hamdy A. Taha; Operations research. ISBN: 0-13-281172-3 (brochada)

Teaching methods and learning activities

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.

Software

Excel Solver

keywords

Physical sciences > Mathematics > Applied mathematics > Operations research
Social sciences > Economics > Management studies > Industrial management

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	30,00
Teste	70,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	110,00
Frequência das aulas	52,00
Total:	162,00

Eligibility for exams

To be able to access the exam the students must obtain a minimum classification of 4 points (out of 14 possible points) in the distributed assessment component (micro-exercises and intermediate test).

In addition to the previous requirement, to attend this curricular unit students must follow FEUP general rules of assessment (in Portuguese https://paginas.fe.up.pt/~contqf/producao/_SERAC/Legislacao/Regulamentos/RegulamentosFEUP/normas%20gerais%20de%20avaliacao.pdf)

Calculation formula of final grade

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).

Examinations or Special Assignments

Students

- Must make an exercise (closed book) at the end of each class.
The 11 exercises will be corrected and graded on a scale from 0 to 100%.
The results will be discussed individually with each student.

- Must make a test (closed book) that will be corrected and graded on a scale from 0 to 4.
The results will be discussed individually with each student.

Special assessment (TE, DA, ...)

These exams will be closed book.

Classification improvement

Students may register for completion, in the last class of the semester, of each of the two components or of the two different components of distributed assessment:

1. The component - exercises - will be assessed through a closed book test (classification from 0 to 10 values).

2. The component - test - will be assessed by a closed-book test (classification of 0 to 4).

In the appeal season the EXAM component will be assessed by means of a closed book exam (classification from 0 to 6).

In all cases the final mark will be calculated as described in the section "Calculation of the Final Classification", considering the best assessment obtained by the student in each component, considering the normal season and the improvement season.

Observations

Demonstration of the syllabus coherence with the curricular unit’s intended learning outcomes
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.
Looking at the definition of operational research at the UK Operational Research Society’s website, we find a perfect match between this scientific field and the declared objectives:

"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 researchgives 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.