Teaching Methodologies
Lectures introduce fundamental concepts, principles, and methods, supported by illustrative examples that contextualize the application of decision support techniques. Theoretical-practical classes focus on structured problem solving, individually or in groups, promoting the development of analytical reasoning skills, selection of appropriate methods and critical interpretation of results.
Students engage in activities, including solving additional exercises, and preparing a group project involving decision-support methods, a written technical report and an oral presentation.
Learning Results
The course aims to equip students with the ability to model, analyse, and solve decision-support problems within Engineering and Industrial Management. Students are expected to understand and formulate deterministic and nondeterministic models, select appropriate solution methods, and assess underlying assumptions. They will develop skills in linear and nonlinear optimization, decision-making under uncertainty, utility theory, queueing models and decision trees. By the end of the course, students should also be able to interpret results, perform sensitivity analyses, and communicate technical conclusions in a rigorous and well-founded manner.
Program
I-Deterministic models
1. Formulation and model development. Models based on linear programming. Sensitivity analysis. Integer programming-based models. 2. Optimization in networks: some models and algorithms. 3. Non-linear models. Formulation of problems. Optimization without restrictions on one or more variables. KKT conditions to nonlinear optimization with constraints. Programming: separable convex quadratic, non-convex.
II-Nondeterministic models
1. Decision theory. Probabilistic and non-probabilistic methods. Decision criteria in uncertainty. Value of information. Utility, indifference and risk. Risk premium. Decision criteria with risk. 2. Queueing models.
III-Decision trees
Decision nodes, states and alternatives. Selection, qualificatio
Curricular Unit Teachers
Maria do Céu Lourenço MarquesGrading Methods
- - Tests and group work or Exam - 100.0%
Internship(s)
NAO
Bibliography
Hillier, F. S., & Lieberman, G. J. (2021). Introduction to operations research (11th ed.). McGraw-Hill.
Winston, W. L. (2022). Operations research: Applications and algorithms (9th ed.). Cengage Learning.
Taha, H. A. (2017). Operations research: An introduction (10th ed.). Pearson.
Clemen, R. T., & Reilly, T. (2014). Making hard decisions with decision tools (3rd ed.). Cengage Learning.
Antunes, C. H. & Tavares, L.T. (Coord.) (2000). Casos de Aplicação da Investigação Operacional. Portugal: McGraw-Hill