| Código |
18145
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| Ano |
1
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| Semestre |
S2
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| Créditos ECTS |
6
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| Carga Horária |
T(30H)/TP(30H)
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| Área Científica |
MECÂNICA COMPUTACIONAL
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Learning outcomes |
Develop 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. Queueing Theory: M/M/1 and M/M/m models, queueing networks, performance simulation.
4. Computational Simulation: Model structuring, random variable generation, results analysis.
5. Numerical Optimization: Unconstrained design (line search, golden section method, conjugate gradient, BFGS); Metaheuristic
optimization (Simulated Annealing).
6. Metaheuristics: Genetic algorithms, particle swarm optimization, tabu search.
7. Decision-Making Based on Artificial Intelligence Algorithms.
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Teaching Methodologies and Assessment Criteria |
Evaluation Criteria:
Analysis and Synthesis Project (TAS): 5 points (25%)
Exercises (EX): 5 points (25%)
Final Project (PF): 5 points (25%)
Individual Written Assessment (PA): 5 points (25%)
Final Grade Formula:
Final Grade = (0.25 × TAS) + (0.25 × EX) + (0.25 × PF) + (0.25 × PA)
Where:
TAS = TAS grade
EX = Exercise grades
PF = Final project grade
PA = Individual written assessment grade
Attendance: 75% minimum.
Minimum Grade for Teaching-Learning Phase: 6.
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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, Dept. de Eng. Electromecânica, Universidade da Beira Interior, Covilhã.
-Hillier, F.S., Lieberman, G.J. (2014). Introduction to Operations Research, 10th Ed. McGraw-Hill.
-Marakas, G.M. (2002). Decision Support Systems, 2nd Ed., PrenticeHall, 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.
-Goodwin, P., Wright, G. (2014). Decision Analysis for Management Judgment, 5th Ed., 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.
-Scientific papers in Scopus and WOS
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| Language |
Portuguese. Tutorial support is available in English.
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