Decision Support Systems

Code:

ESG0005

Acronym:

SAD

Keywords
Classification	Keyword
OFICIAL	Economics
OFICIAL	Mathematics

Instance: 2023/2024 - 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	19	Syllabus since 2007/08	1	-	6	42	162

Teaching language

English

Objectives

 

At the end of the course, students should be able to:

- understand the complexity and the qualitative aspects of decision making processes;

- 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;

- develop models and optimization algorithms, as well as heuristic and simulation approaches to solve problems with a practical interest, in Operations Management.

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.

 

Learning outcomes and competences

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.

 

Working method

Presencial

Program

1. 

Organizations and decision processes. Decision Support System (DSS): general structure and components. Models. Qualitative aspects in decision making. Structuring of decision problems.  The use of spreadsheets.

2.

Topics in Decision Theory and Multicriteria Analysis. Situations of uncertainty and risk. Decision trees. Decision problems with multiple criteria. Analytic Hierarchy Process (AHP). Sensitivity analysis and “what-if” analysis. Scenario analysis.

3. 

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

Transportation and assignment problems. 

 

4. 

Network problems: flows, paths, and trees - models and algorithms. 

Combinatorial optimization problems: heuristic method for practical problems solution - design and implementation. 

Integer programming models. 

5. 

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

6. 

DSS design methodologies and implementation tools. 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

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

The course unit is organized in weekly sessions 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

Designation	Weight (%)
Exame	65,00
Trabalho escrito	35,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	120,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

General rules of evaluation, article 4th.

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 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.