Decision Support Systems

Code:

ESG0005

Acronym:

SAD

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2010/2011 - 1S

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

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MESG	17	Syllabus since 2007/08	1	-	6	56	162

Teaching language

Portuguese

Objectives

- understand the complexity and quantitative and qualitative aspects of decision making processes, using simple approaches for their structuring;
- be able to create spreadsheet models and develop tools for decision support;
- use general concepts and principles of Decision Theory and Multicriteria Analysis, to structure decision alternatives and criteria;
- be able to perform data analysis and modelling, as well as sensitivity and scenario analysis;
- use optimization, heuristic and simulation models and algorithms, to suppor the solution of decision problems;
- understand the problems associated with the design of Decision Support Systems e be able to contribute to their specification.

Program

1.
Organizations and decision processes. Decision levels, complexity and analysis paradigms.
The role of quantitative models and methods in decision making.
The use of spreadsheets.

2.
Structuring of decision processes. Influence diagrams.
Fundamental skills in spreadsheet modelling.
Organization of models in spreadsheets.

3.
Formulation of problems and optimization models: linear programming and extensions. Optimization methods and computational tools for optimizations.
Transportation and assignment problems.

4.
Network problems: floex, paths; trees - models and algorithms.
Combinatorial optimization problems: heuristic method for practical problems solution - design and implementation.
Integer programming models.

5.
Decision Theory and Multicriteria Analysis topics. Situations of risk and uncertainty.
Structuring of decision alternatives and criteria. Decision trees. Decision problems with multiple criteria. Sensitivity and "what-if" analysis.

6.
Simulation models: general structure and domain of application. Queues: brief introduction to theory, models and applications. Statistical and event-driven simulation. Simulation and decision support.

Mandatory literature

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

Complementary Bibliography

Clemen, Robert T.; Making hard decisions. ISBN: 0-534-92336-4
Tavares, Luís Valadares 070; Investigação operacional. ISBN: 972-8298-08-0
Antunes, Carlos Henggeler 340; Casos de aplicação da investigação operacional. ISBN: 972-773-075-2

Teaching methods and learning activities

Classes will be used to introduce the program topics, present and discuss cases, and solve small illustrative problems.
Some sessions may be occupied with student presentations and subsequent discussion.
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

General rules of evaluation, article 4th.

Calculation formula of final grade

FE (final exam, closed book) - 0 to 20 points (minimum 8)
A1 (on program topics 1 and 2) - 0 to 20 points
A2 (on program topics 3 and 4) - 0 to 20 points
A3 (on program topics 5 and 6) - 0 to 20 points


Final grade (before rounding):
0.55 FE + 0.15 A1 + 0.15 A2 + 0.15 A3

Examinations or Special Assignments

The assignments evaluation may include brief discussions with the students.

Special assessment (TE, DA, ...)

Evaluation identical to normal case.
All students (including working students, ...) must deliver the assignments in the established date.

Classification improvement

Exam only.
The students may not repeat components A1, A2, and A3 of evaluation.