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Decision Methods

Code: L.EA020     Acronym: MD

Keywords
Classification Keyword
OFICIAL Technological Sciences - Engineering

Instance: 2024/2025 - 1S Ícone do Moodle

Active? Yes
Web Page: https://sites.google.com/gcloud.fe.up.pt/io-md
Responsible unit: Department of Industrial Engineering and Management
Course/CS Responsible: Bachelor in Environmental Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
L.EA 39 Syllabus 3 - 6 52 162
L.EMG 17 Plano de estudos oficial a partir de 2008/09 2 - 6 52 162

Teaching Staff - Responsibilities

Teacher Responsibility
Sara Sofia Baltazar Martins

Teaching - Hours

Lectures: 2,00
Recitations: 2,00
Type Teacher Classes Hour
Lectures Totals 2 4,00
Sara Sofia Baltazar Martins 2,00
Ricardo Filipe Ferreira Soares 2,00
Recitations Totals 2 4,00
Ricardo Filipe Ferreira Soares 2,00
Sara Sofia Baltazar Martins 2,00

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). contexts. It also emphasizes the importance of sustainability by integrating the United Nations Sustainable Development Goals (SDGs) to ensure that the proposed solutions contribute to a more sustainable and equitable future.


SPECIFIC OBJECTIVES

The main objective of this course is to develop skills for analyzing a wide range of real-world situations by creating
models. These skills are based on identifying the key problem in an unstructured situation, developing a framework to analyze and address it, and applying analytical methods to solve it.


LEARNING OBJECTIVES

Endow the students with the skills to:


  • Identify and address decision problems in a skillful and
    structured manner;

  • Build models of decision problems;

  • Integrate sustainability considerations into decision
    models and align proposed solutions with the Sustainable Development Goals (SDGs);

  • Identify and use analytical methods to obtain solutions
    to the constructed models to support informed decisions;

  • Use computational tools to analyze and obtain solutions for the constructed models;

  • Extract information from the models and use it to
    communicate and motivate organizational change.

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 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.
In addition, students are encouraged to integrate sustainability and SDG-aligned considerations into their analysis and problem formulation.
 

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.


Students will be encouraged to consider the implications of the decisions in terms of sustainability, specifically the impact of their models and solutions on areas such as health, education, equality, and the preservation of the environment.
 

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

optimization | linear models
optimization | linear models | network problems
optimization | models with integer variables
resolution of optimization problems | without integer variables
   - graphic analysis + sensitivity analysis
   - excel solver
   - solver sensitivity report
resolution of optimization 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

Teaching methods and learning activities

Presentation of course unit themes, using methods of active learning whenever possible, illustrated by cases, examples and problems.

The problems from a list of proposed problems are discussed and solved by the students.

Software

Solver do Microsoft Excel

keywords

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

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Teste 100,00
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 admitted to the appeal exam, the students must obtain a minimum classification of 4 points (out of 20 possible points) in the distributed assessment (micro-exercises and tests).

According to the General Evaluation Rules of FEUP- Article 4

Calculation formula of final grade

DISTRIBUTED EVALUATION without FINAL EXAM


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%.

Two tests (closed book) (0 to 5 points each).

Examinations or Special Assignments

Students enrolled in ORDINARY REGIME

 

Must make an exercise (closed book) at the end of each class. 

The exercises will be corrected and graded on a scale from 0 to 100%.

The results will be discussed with the students.


Must make two tests (closed book), that will be corrected and graded on a scale from 0 to 100%.

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 tests will make a closed book examination with two components:
  - the component corresponding to the micro-exercises (graded from 0 to 10)
  - component corresponding to the two tests (graded from 0 to 10)

 

Special assessment (TE, DA, ...)

These exams will be closed book.

Classification improvement

The appeal exam will have two components:
- the component corresponding to the micro-exercises (closed book and graded from 0 to 10)
- component corresponding to the two tests (closed book and graded from 0 to 10)

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