|OFICIAL||Quantitative Methods and Management|
|Responsible unit:||Department of Industrial Engineering and Management|
|Course/CS Responsible:||Master in Informatics and Computing Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|MIEIC||0||Syllabus since 2009/2010||5||-||6||56||162|
|Jorge Manuel Pinho de Sousa|
At the end of the course, students should be able to:
understand the complexity and the qualitative aspects of decision making processes, and to use problem structuring techniques and multicriteria approaches; define the structure and the components of a Decision Support System (DSS), as well as using methodologies and techniques to design and implement DSSs; develop spreadsheet models, and design tools to support decision-making; use the main concepts of Decision Theory and Multicriteria Analysis, to structure alternatives and decision criteria; develop models and optimization algorithms, as well as heuristic approaches to solve problems with a practical interest, particularly in the context of Operations Management and Combinatorial Optimization; develop simulation models and design Interactive Visual Simulation Systems.
The competences to be acquired by the students, as well as the results of the learning process, derive directly from the satisfaction of the indicated objectives.
Organizations and decision processes. Decision Support System (DSS): general structure and components. Quantitative methods for decision making. Operations Research Methodology. Models. Qualitative aspects in decision making. Structuring of decision problems.
Topics in Decision Theory and Multicriteria Analysis. Situations of uncertainty and risk. Structuring of decision alternatives and criteria. Decision trees. Decision problems with multiple criteria. Analytic Hierarchy Process (AHP). Sensitivity analysis and “what-if” analysis. Scenario analysis.
Operations Management and Combinatorial Optimization problems: models and applications. Meta-heuristics: local search algorithms, "simulated annealing", tabu search. Genetic Algorithms. Integration of these algorithms in DSSs.
Simulation models: general structure and application domain. Interactive visual simulation. Applications.
DSS design methodologies and implementation tools. Modularity and prototyping. Organizational aspects in DSS design. DSS specification and development: examples.
Presentation and discussion of case studies.
The course unit is organized in a weekly session of 3 hours, used to introduce the program topics, present and discuss cases, and solve small illustrative problems.
The reports to be presented (as part of evaluation) will essentially be developed out of class.
|Frequência das aulas||42,00|
Do not exceed the limit of absences and have a minimum of 7.5 iin each of the evaluation components.
FE (final exam) - 0 to 20 points (minimum 7.5)
A (assignment) - 0 to 20 points (minimum 7.5)
Final grade (before rounding): 0.65 FE + 0.35 A
FE - final exam, closed book
A - assignment, to be done in groups of 2 students (the assignment assessment may include a brief discussion session)
Evaluation identical to the normal case.
The improvement of the final grade can only be done on the exam component.
Students cannot repeat the assignment.
The formula for the final grade is the same.