Decision Methods
Keywords |
Classification |
Keyword |
OFICIAL |
Social Science |
Instance: 2020/2021 - 1S
Cycles of Study/Courses
Teaching language
Portuguese
Objectives
BACKGROUND
This course focuses on the application of analytical methods to take 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, 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:
- 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 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 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 on-going 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 may be 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 with 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 web site, 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
- linear programming
- graphic resolution + sensitivity analysis
- Simplex method fundamentals
- resolution of models | without integer variables
- Excel solver
- solver sensitivity report
- modelling | problems in networks
- modelling | with whole variables
- modelling | with whole auxiliary variables
- resolution of problems with integer variables | branch & bound
- resolution of problems with integer variables | solver
- 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
Teaching methods and learning activities
Theoretical classes: Presentation of course unit themes, using methods of active learning whenever possible, illustrated by cases, examples and problems.
Practical classes: Presentation and application of algorithms and methods and clarification of doubts about the resolution of the proposed problems. Follow-up of the students regarding the assignments and homework.
Software
Solver do Microsoft Excel
keywords
Social sciences > Economics > Management studies > Industrial management
Physical sciences > Mathematics > Applied mathematics > Operations research
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
According to General Evaluation Rules of FEUP- Article 4
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 enrolled in ORDINARY REGIME
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 with the students.
Must make a test (closed book), that will be corrected and graded on a scale from 0 to 4.
The results will be discussed with the students.
Students who have no obligation to attend classes and that did not make the exercises and the test will make an examination with three components:
- DISTRIBUTED EVALUATION - exercises
- DISTRIBUTED EVALUATION - test;
- EXAM.
The DISTRIBUTED EVALUATIONS will be closed book.
The EXAM component will be open book.
Classification improvement
Students may improve their marks in each one of the evaluation components:
- The component DISTRIBUTED EVALUATION - exercises will be assessed through a closed-book exam (rating 0 to 10 points).
- The component DISTRIBUTED EVALUATION - test will be assessed through a closed-book exam (rating 0 to 4 points).
- The component EXAM will be open book (rating 0 to 6 points).
In all cases, the final classification will be calculated as explained in section "Final Classification", considering the best evaluation obtained by the student in each component.