Decision Support Systems

Code:

ESG0005

Acronym:

SAD

Keywords
Classification	Keyword
OFICIAL	Economics
OFICIAL	Mathematics

Instance: 2024/2025 - 1S

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

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MESG	27	Syllabus since 2007/08	1	-	6	42	162

Teaching language

English

Objectives

Course Overview:

This course explores Decision Support Systems (DSS) and their application in Service Engineering and Management, focusing on techniques such as linear programming, sensitivity analysis, Python-based optimization, heuristics, multicriteria decision-making, and decision theory. Examples related to the Sustainable Development Goals (SDGs) will be integrated throughout the course to showcase how DSS can address global challenges.

Learning Objectives:

By the end of the course, students will:

• Build and solve linear programming models.
• Conduct sensitivity analysis using Excel Solver.
• Implement optimization techniques using Python.
• Use heuristic methods for solving complex optimization problems.
• Apply multicriteria decision-making tools, decision theory, and decision trees.
• Make decisions under uncertainty and experimentation, with a focus on real-world applications.

Learning outcomes and competences

Learning Outcomes:

By the end of this course, students should be able to:

1. Apply linear programming techniques to formulate and solve optimization problems in Service Engineering and Management.


2. Perform sensitivity analysis using Excel Solver to evaluate the robustness of optimization solutions.


3. Implement optimization techniques using Python to solve complex optimization problems.


4. Apply heuristic methods to address combinatorial optimization problems that exact methods cannot solve.


5. Use multicriteria decision-making tools and decision theory to evaluate alternatives in scenarios with multiple objectives.


6. Build and analyze decision trees for decision-making under uncertainty, considering risks and benefits.


7. Develop and apply experimentation strategies to make decisions in uncertain and dynamic environments, focusing on real-world problems.

Competencies:

Throughout the course, students will develop the following competencies:

1. Technical proficiency in using optimization tools, such as Excel Solver and Python libraries (PuLP), to solve problems.


2. Analytical skills to interpret and evaluate optimization solutions and decision-making in complex contexts.


3. Informed decision-making abilities, applying both quantitative and qualitative methods to solve real problems and implement efficient strategies.


4. Problem-solving skills by applying heuristics and optimization models to practical problems.


5. Collaboration and teamwork abilities, especially in group projects, integrating different perspectives.


6. Critical and creative thinking skills to handle complex and dynamic decisions, including uncertainty and experimentation scenarios.

 

Working method

Presencial

Program

Introduction to Decision Support Systems (DSS):

• Key Decision Support Systems (DSS) concepts and their application in Service Engineering and Management.


• The importance of DSS in supporting decision-making in complex and dynamic environments.

Linear Programming:

• Introduction to linear programming (LP) and model formulation.


• Solving LP problems using Excel Solver and Python.


• Interpretation and analysis of the results.

Sensitivity Analysis:

• Importance of sensitivity analysis in evaluating the robustness of solutions.


• Using Excel Solver to perform sensitivity analysis.


• Interpreting results to support decision-making.

Optimization with Python:

• Introduction to Python for optimization.


• Solving linear programming problems with Python libraries (such as PuLP).


• Comparison between optimization tools (Excel Solver vs. Python).

Heuristics for Combinatorial Optimization Problems:

• Introduction to combinatorial optimization and its complexity.


• Applying heuristic methods, such as greedy algorithms, genetic algorithms, and simulated annealing.


• Addressing practical problems using heuristics.

Multicriteria Decision Support Systems (MCDSS):

• Concepts of multicriteria decision-making.


• Applying multicriteria decision-making methods (AHP, TOPSIS, etc.).


• Building and evaluating decision support models based on multiple criteria.

Decision Theory and Decision Trees:

• Fundamentals of decision theory.


• Developing and analyzing decision trees to evaluate alternatives.


• Applying utility theory and risk management in decision-making.

Decisions with Experimentation and Uncertainty:

• Decision-making strategies involving experimentation (e.g., A/B testing).


• Decision-making under conditions of uncertainty and risk.


• Introduction to Bayesian analysis and updating probabilities with new data.

Final Project:

• Applying the knowledge acquired to solve a real-world problem using the techniques learned throughout the course.

Mandatory literature

Powell, Stepehn G.; Management Science. ISBN: 978-0-470-03840-6

Complementary Bibliography

Clemen, Robert T.; Making hard decisions. ISBN: 0-534-92336-4
Tavares, Luís Valadares 070; Investigação operacional. ISBN: 972-8298-08-0
Antunes, Carlos Henggeler 340; Casos de aplicação da investigação operacional. ISBN: 972-773-075-2

Teaching methods and learning activities

The course will be conducted through a combination of theoretical and practical teaching methods, emphasizing the application of concepts discussed in class. The following approaches will be used:

Interactive Theoretical Components:

The theoretical components of the classes will be delivered using presentations and practical examples, encouraging active participation from students. The professor will present the key concepts and discuss applications in the context of Service Engineering and Management. Questions and discussions will be posed during the classes to stimulate critical thinking and problem-solving skills.

Case Studies:

Students will be exposed to small real and simulated cases, where they will apply the techniques learned to solve practical problems. These case studies will be discussed and solved in groups.

Practical Classes and Software Exercises:

There will be practical sessions where students will use tools such as Excel Solver and Python to solve optimization and simulation problems. Through guided exercises, students will be encouraged to implement the models discussed in class, exploring their functionalities and limitations.

Formative Assessments:

Small individual assessments in each class will allow for measuring the understanding of the concepts throughout the course. These assessments will include theoretical and practical questions and aim to identify areas requiring more attention. These assessments will also encourage continuous student participation and regular material review.

Discussion and Feedback Sessions:

Continuous feedback will be provided during practical activities and group projects, ensuring students fully understand the concepts.

Software

Microsoft Excel
Phyton

keywords

Physical sciences > Mathematics > Applied mathematics > Operations research

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	25,00
Trabalho escrito	25,00
Teste	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	120,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

To obtain attendance in this course unit, students must comply with the provisions of the general assessment regulations of FEUP (https://paginas.fe.up.pt/~contqf/producao/_SERAC/Legislacao/Regulamentos/RegulamentosFEUP/normas%20gerais%20de%20avaliacao.pdf).

Calculation formula of final grade

DISTRIBUTED ASSESSMENT
(no reference materials) (graded between 0 and 15 points)

Micro-exercises (graded between 0 and 10 points)

• Each exercise will be graded on a scale from 0 to 100%.
• The final grade for this component will be the sum of the grades obtained by each student in the micro-exercises assigned at the end of each class, excluding the two lowest grades.

Mid-term Test (graded between 0 and 5 points)

Group Project (graded between 0 and 5 points)

Examinations or Special Assignments

Group Project

 

Special assessment (TE, DA, ...)

Assessments in the special exam period will be conducted through a closed-book exam.

Classification improvement

The improvement of the final grade can only be applied to the components of the micro-exercises and the mid-term test.

Students cannot repeat the Group Project.

The formula for calculating the final grade remains the same.