Decision Models


The objective of this curricular unit is to develop decision-making competencies applied to the industrial and service context. It is intended to teach how to structure a problem so as to choose the appropriate methods to support them in decision making, as well as how to apply the methods and analyze the results. Problems will be addressed in a deterministic context, with a single decision criterion as well as considering several decision criteria. To deal with contexts of uncertainty, different methods of decision-making will be taught, with different levels of complexity,

Students should also develop teamwork skills to understand, formulate and solve problems in a real context, as well as develop oral and written communication skills to describe problems, their approach to support the decision-making process, and justification of options.

General characterization





Responsible teacher

António Carlos Bárbara Grilo


Weekly - 4

Total - 86

Teaching language



Students should have basic knowledge of Linear programming.


All content (theory and exercises are on the UC website at

Additional content can be found in the following books:

1) Applied Management Science: Modeling, Spreadsheet Analysis, and Communication for Decision Making,2nd Edition, 

John A. Lawrence, Barry A. Pasternack, ISBN: 978-0-471-39190-6, February 2002

2) Management Decision Making: Spreadsheet Modeling, Analysis, and Application

George E. Monahan

ISBN 10: 0521781183  ISBN 13: 9780521781183

Publisher: Cambridge University Press, 2000

Teaching method

Teaching method:
·      Lectures;
·      Discussion of case studies with students;
·      Problem solving sessions;
·      Team work;
·      Presentation and discussion of team works;
·      Assessment.

During the year of 2020/21 all Theoretical and Practical Classes will ve delivered thorugh digital platforms (ZOOM, Google Meet and Moodel).

Evaluation method

The final evaluation of the curricular unit of Decision Models (MDc) will be based on the following elements:

- 1 Test (T1), with 50% of weight in the final evaluation

- 1 Group Assignment (TG). It consists in the development of an asssignment to be carried out according to the specified requirements. The groups will have a maximum of 5 students.

The final grade of MDc will be composed as follows:

Final score = 0.50 * T1 + 0.50 * TG

T1 - test 1 score
TG - group assignment score

The grade of each of the assessment components is rounded to the tenths.

Approval occurs if the Final Mark is equal to or greater than 9.5 points, along with each assessment element that must be equal or higher to 9,5.

Subject matter

1. Introduction to how we make decisions. Phases of the decision process. Type of decision-making environments: certainty, risk and uncertainty.

2. Deterministic decision models. Selection and evaluation of alternatives. Formulation and development of the model. Models based on linear programming. Sensitivity analysis. Analysis of large variations.

3. Decision criteria in uncertainty. Value of information. Decision criteria with risk.

4. Decision trees. Nodes, alternatives and states. Selection, qualification and valuation of alternatives. Bayesian analysis in the estimation of probabilities. Value of Perfect and Imperfect Information.

5. Value Functions. Utility, indifference and risk. Risk premium.

6. Decision-making with multiple criteria. The AHP method. The TOPSIS method. Multi-Objective Methods.

7. Monte Carlo simulation. Applications in the Evaluation of Investment Projects.


Programs where the course is taught: