Decision Support Systems

Code:

EIC0074

Acronym:

SADE

Keywords
Classification	Keyword
OFICIAL	Quantitative Methods and Management

Instance: 2011/2012 - 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	24	Syllabus since 2009/2010	5	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

The general philosophy, structure and components of a Decision Support System (DSS) will be studied, as well as methodologies and techniques that will alow the students to design and implement DSSs.

The 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. Supported by spreadsheets, students should be able to develop data modelling and analysis, as well as scenario analysis.

They will also become familiar with Combinatorial Optimization models and applications, and generic heuristics techniques to solve problems with a practical interest, particularly in the context of Operations Management,

DSS examples will be presented and discusses, with an emphasys on user interface and Interactive Visual Simulation Systems.

Program

1.
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).

3.
Data modelling and analysis. Spreadsheet engineering. Sensitivity and "what-if" analysis. Scenario analysis.

4.
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.

5.
Simulation models: general structure and application domain. Interactive visual simulation. Applications.

6.
DSS design methodologies and implementation tools. Modularity and prototyping. Organizational aspects in DSS design. DSS specification and development: examples.

7.
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
	Total:	-	0,00

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, closed book) - 0 to 20 points (minimum 7.5)
A (assignments A1 and A2) - 0 to 20 points (minimum 7.5)

Final grade (before rounding):
0.50 FE + 0.50 A

Examinations or Special Assignments

FE - final exam, closed book

A1 - assignment on program topics 1, 2 and 3

A2 - assignment on program topics 4, 5 and 6

Special assessment (TE, DA, ...)

Evaluation identical to normal case.

Classification improvement

The improvement of the classification may consist of: only the final exam, with the distributed component remaining unchanged; only the distributed component, with the final exam remaining unchanged; both final exam and distributed component.
The distributed component will be evaluated with a special assignment during the exams period.
In all situations the formula for the final grade is the same.