Decision Support Systems

Code:

ESG0005

Acronym:

SAD

Keywords
Classification	Keyword
OFICIAL	Economics
OFICIAL	Mathematics

Instance: 2025/2026 - 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 introduces Decision Support Systems (DSS) as a bridge between analytical models, data, and managerial decision-making. It provides a balanced perspective of decision theory foundations, quantitative optimization models, behavioral aspects of decision-making, and practical DSS design and evaluation. Students will learn to structure decision problems, apply optimization and heuristic approaches, recognize human biases, and critically assess DSS applications in various domains.

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:

• Understand DSS architecture and types (model-driven, data-driven, knowledge-driven, communication-driven).
• Apply decision theory methods under certainty, risk, and uncertainty, including expected utility and decision trees.
• Use multi-criteria decision-making techniques (Weighted Sum, AHP).
• Identify common behavioral biases and explain how they influence decision-making.
• Formulate and solve optimization problems (linear programming, network models) using Excel Solver.
• Understand heuristic and metaheuristic approaches for complex problems.
• Design and evaluate a DSS, considering usability, adoption, and ethical aspects.
• Apply DSS concepts to real-world problems and present results in team projects.

Learning outcomes and competences

Learning Outcomes:

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

• Formulate decision problems and apply appropriate decision-making models.


• Build and analyze decision trees, evaluate expected utility, and apply value of information.


• Apply multi-criteria decision-making techniques to evaluate alternatives.


• Recognize behavioral biases and interpret their implications for DSS design and use.


• Formulate and solve linear programming and network optimization problems.


• Apply heuristic methods to combinatorial problems.


• Design, propose, and evaluate DSS architectures.


• Critically assess DSS applications across domains such as transport, healthcare, and business.

Competences:

Throughout the course, students will develop the following competencies:

• Analytical and problem-structuring skills.


• Technical proficiency in optimization tools (Excel Solver, optional Python).


• Understanding of behavioral decision-making limitations.


• DSS design, evaluation, and communication skills.


• Collaboration and teamwork in group projects.


• Critical thinking applied to ethics and decision-making

 

Working method

Presencial

Program

1. Introduction to DSS: concepts, architecture, types, and applications.


2. Decision Theory (I): decisions under certainty, risk, and uncertainty; payoff tables; expected utility.


3. Decision Theory (II): decision trees, risk attitudes, value of information.


4. Multi-Criteria Decision-Making (MCDM): weighted sum model, Analytic Hierarchy Process (AHP).


5. Behavioral Decision-Making: bounded rationality, prospect theory, biases (anchoring, framing, availability), group decision-making.


6. Quantitative Models (I): linear programming; formulation and solving with Excel Solver.


7. Quantitative Models (II): network optimization problems (shortest path, assignment, transportation).


8. Heuristics & Metaheuristics: greedy algorithms, local search, genetic algorithms, simulated annealing (overview and examples).


9. Designing, Building, and Evaluating a DSS: DSS architecture, development process, evaluation (effectiveness, usability, adoption, ethics).


10. Applications & Ethics: DSS in transport, healthcare, business, and sustainability; challenges and ethical considerations.


11. Group Project Presentations: applied DSS case studies.

Mandatory literature

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

Complementary Bibliography

Daniel Kahneman; Thinking, fast and slow, Farrar, Straus and Giroux, 2011
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:

• Lectures: presentation of concepts, illustrated by examples and case discussions.
• Practical sessions: Excel Solver and small heuristic problems to implement and interpret models.
• Case studies: applied group work in different domains (transport, healthcare, business).
• Group project: design and presentation of a DSS solution.
• Continuous feedback: discussion of results during exercises and project development.

Software

Microsoft Excel
Phyton

keywords

Physical sciences > Mathematics > Applied mathematics > Operations research

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Participação presencial	10,00
Trabalho de campo	10,00
Trabalho prático ou de projeto	30,00
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 evaluation with final exam.

 

Assessment Components:

 

• In class work: 10%
• Homework assignments: 10%
• Group Project (3 or 4 students): 30%
• Exam: 50%

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.