| Code |
17642
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
T(30H)/TP(30H)
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| Scientific area |
Engenharia e Gestão Industrial
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|
Entry requirements |
Not applicable.
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Learning outcomes |
To develop the student´s skills for formulating and solving complex decision-making problems using quantitative methods based on
optimization techniques, network analysis, and simulation. Prepare students to apply mathematical models and algorithms to engineering
and management contexts, promoting critical analysis and efficiency in decision-making processes. Cover topics such as linear and
nonlinear programming, network optimization, queueing theory, heuristics, and metaheuristics, emphasizing the integration of emerging
technologies in decision making based in artificial intelligence algorithms and computational support tools. Foster the ability to interpret
results and propose evidence-based solutions, considering practical scenarios and real-world constraints.
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Syllabus |
1. Optimization: Linear programming (graphical method, simplex algorithm, sensitivity analysis), nonlinear programming (basic numerical
methods).
2. Networks and Graphs: Maximum flow, shortest path, minimum spanning tree.
3. Project Management: CPM/PERT, task scheduling, resource allocation.
4. Queueing Theory: M/M/1 and M/M/m models, queueing networks, performance simulation.
5. Computational Simulation: Model structuring, random variable generation, results analysis.
6. Metaheuristics: Genetic algorithms, particle swarm optimization, tabu search.
7. Decision-Making Based on Artificial Intelligence Algorithms.
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Main Bibliography |
Gaspar, P.D., Lima, T.M. (2020). Caderno Teórico de Métodos Quantitativos de Apoio à Decisão -Diapositivos de acompanhamento e
apoio às aulas, Departamento de Engenharia Electromecânica, Universidade da Beira Interior, Covilhã, 392 páginas.
Hillier, F.S., Lieberman, G.J. (2014). Introduction to Operations Research, 10th Ed. McGraw-Hill.
Marakas, G.M. (2002). Decision Support Systems, 2nd Ed., Prentice Hall, 2002.
Keller, J.M., Liu, D., Fogel, D.B. (2016). Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary
computation. Wiley.
Law, A.M. (2024). Simulation Modeling and Analysis. McGraw-Hill.
Paul Goodwin, P., Wright, G. (2014). Decision Analysis for Management Judgment (5th Edition), Wiley.
Belton, V., Stewart, T. (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Springer.
Hammond, J.S. Keeney, R.L., Howard Raiffa, H. (2015). Smart Choices: A Practical Guide to Making Better Decisions. Harvard Business
Review Press.
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Teaching Methodologies and Assessment Criteria |
Considering the modular nature of the curriculum, students are assessed continuously through an analysis and synthesis project (TAS),
three individual practical exercises, a written knowledge assessment, and a final project. The TAS and final project are conducted in groups
of four. Attendance at contact hours is mandatory. The minimum passing grade is 10 (out of 20). All evaluation components must be completed, with each requiring a minimum grade of 6 to qualify for the final examination. The final grade calculation formula applies during the teaching-learning period and examination periods. Grade improvement requires a final exam. Oral presentations and submissions of the
TAS and final project are mandatory.
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Language |
Portuguese. Tutorial support is available in English.
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