Métodos de Apoio à Decisão

The teaching and learning methodologies of this course align with a student-centred pedagogical model, emphasising active learning and the development of skills for solving real-world problems in Engineering and Physical Asset Management. The theoretical classes adopt an expository-dialogic approach, introducing concepts of modelling and optimisation through concrete situations, encouraging participation, question formulation, and the progressive construction of mathematical models by students. In the theoretical-practical classes, priority is given to problem-solving, case studies, and small project tasks, where groups of students formulate, implement, and interpret models of linear programming, network optimisation, and non-linear optimisation. Whenever possible, these activities take place in a computer laboratory environment, using optimisation software and digital decision-support tools, fostering the connection between theory and practice and developing digital literacy. Activities will also be proposed where students utilise Artificial Intelligence tools critically and responsibly, comparing solutions obtained independently with those generated by AI, validating results, identifying limitations, and discussing ethical and professional implications. These strategies reinforce the student’s role as an active agent in their learning and develop critical thinking, mathematical communication, and collaborative working skills. Throughout the semester, the lecturer ensures frequent formative feedback, both in face-to-face settings and via the institutional digital platform, encouraging self-regulation of learning and reflection on problemsolving processes. In this way, the adopted methodologies embody the institution’s pedagogical model, promoting the integration of knowledge, technical skills, and professional attitudes within Decision Support Methods.

The course aims to equip students with theoretical and practical knowledge of optimisation methods applied to problems in Engineering and Physical Asset Management. By the end of the course, students should be able to:
• Think and reason mathematically, formulate and solve mathematical problems, model real situations, and represent them rigorously using symbols and mathematical formalism.
• Communicate in, with, and about mathematics, utilising different representations, digital resources, and support tools.
• Formulate and solve linear programming and network optimisation problems, applying appropriate methods (Simplex, transportation, assignment, and shortest path).
• Use Artificial Intelligence tools critically and responsibly, recognising limitations, validating results, and reflecting on ethical and professional implications.

1. Introduction
2. Linear Programming. Simplex Method and Variants
3. Transportation Problems
4. Assignment Problems
5. Network Problems
6. Non-linear Optimisation and Heuristic Methods
7. Fundamental Concepts of Project Management
8. Organisational Aspects
9. Methodologies
10. Graphical Representation
11. Planning and Resource Control
Throughout the semester, software will be used whenever possible to support understanding and solving the problems under study.

• - Classwork, Tests and Exam - 100.0%

NAO