Operational Research

Code:

L.EGI025

Acronym:

IO

Keywords
Classification	Keyword
OFICIAL	Statistic and Operational Research

Instance: 2024/2025 - 1S

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

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L.EGI	118	Syllabus	3	-	6	52	162

Teaching language

Portuguese

Objectives

BACKGROUND

This course focuses on the application of analytical methods fo

better decision-making and provides students with modeling

and optimization tools that will be highly useful in addressing

and solving organizational problems. In addition, it covers

concepts related to uncertainty management in various

organizational 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 i

an unstructured situation, developing a framework to analyze

and address it, and applying analytical methods to solve it.

LEARNING OBJECTIVES

Provide 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 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.
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

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.

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.

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.

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
modelling | network problems
modelling | with integer variables
modelling | with integer auxiliary variables
resolution of models | without integer variables
          - linear programming
          - graphic resolution + sensitivity analysis
          - simplex method fundamentals
          - Excel solver
          - solver sensitivity report

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

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

PuLP
Excel Solver

keywords

Physical sciences > Mathematics > Applied mathematics > Operations research

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Teste	70,00
Exame	30,00
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).

Calculation formula of final grade

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 (closed book) (0 to 6 points).
Note: in the exam, students will be able to consult a double-sided A4 sheet of paper that they have prepared themselves.

Examinations or Special Assignments

Micro-exercises at the end of each class.
The micro-exercises will be corrected and graded on a scale from 0 to 100%.
The results will be discussed individually with each student.

Intermediate test
The intermediate test will be corrected and graded on a scale from 0 to 4.
The results will be discussed with the students.

Internship work/project

Not applicable

Special assessment (TE, DA, ...)

These exams will be closed book. Students will only be able to consult a double-sided A4 sheet of paper that they have prepared themselves.

Classification improvement

Students will be able to independently improve each of the assessment components in the appeal season:

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).


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

Note: in the exam, students will be able to consult a double-sided A4 sheet of paper that they have prepared themselves.

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.