Decision Support Systems

Code:

EIC0074

Acronym:

SADE

Keywords
Classification	Keyword
OFICIAL	Quantitative Methods and Management

Instance: 2012/2013 - 1S

Active?	Yes
Responsible unit:	Department of Industrial Engineering and Management
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEIC	19	Syllabus since 2009/2010	5	-	6	56	162

Teaching language

Portuguese

Objectives

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 and simulation 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.

Learning outcomes and competences

NA.

Working method

Presencial

Program

1.

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.

 

2.

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.

 

3.

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.

 

4.

Simulation models: general structure and application domain. Interactive visual simulation. Applications.

 

5.

DSS design methodologies and implementation tools. Modularity and prototyping. Organizational aspects in DSS design. DSS specification and development: examples.

 

6.

Presentation and discussion of case studies. 

Mandatory literature

Powell, Stepehn G.; Management Science. ISBN: 978-0-470-03840-6

Complementary Bibliography

Talbi, El-Ghazali 1965-; Metaheuristics. ISBN: 978-0-470-27858-1
Turban, Efraim; Decision support and expert systems. ISBN: 0-13-320383-2
Hillier, Frederick S.; Introduction to operations research. ISBN: 0-07-100745-8

Teaching methods and learning activities

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.

Software

Microsoft Excel

keywords

Physical sciences > Mathematics > Applied mathematics > Operations research

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	42,00
	Exame		65,00
	Trabalho escrito		35,00
	Total:	-	100,00

Eligibility for exams

Do not exceed the limit of absences and have a minimum of 7.5 in the assignments.

Calculation formula of final grade

FE (final exam) - 0 to 20 points (minimum 7.5)

A (assignment) - 0 to 20 points (minimum 7.5)

Final grade: 0.65 FE + 0.35 A

Examinations or Special Assignments

FE - final exam, closed book

A - assignment, to be done in groups of 2 students (the assignment assessment may include a brief discussion session)

Special assessment (TE, DA, ...)

Evaluation identical to the normal case.

Classification improvement

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.