Decision Support Techniques

The classes in this course are theoretical and practical in nature.

At first, the classes are more expository, aimed at establishing the theoretical basis of the subjects covered, followed by the practical component, with the application to complex decision problems in the field of Management, aimed at a prescriptive analysis of these problems. The mathematical resolution of these problems will use specific software, including Python optimization libraries, seeking to answer large-scale problems within the scope of the themes proposed in the P5 syllabus.

The following teaching-learning methodologies will be used:

ME1 – Expository: presentation of concepts, techniques and methods, with a strong focus on practical applications.

ME2 – Participative: in-class discussion of cases within the scope of the themes in the P5 syllabus, developing skills in prescriptive analysis.

ME2 – Active: use of software to support the resolution of larger problems, including Python, allowing laboratory spaces that encourage the autonomous use of these computer applications.

Learning objectives:

LO1 – Introduction of mathematical modelling techniques aimed at complex decision problems in Planning and Management, using linear and integer linear programming.

LO2 – Solving applied cases, focusing on: project selection, production management, financial management, project planning and work schedules and shifts, among others.

LO3 – Using computer optimization tools to solve the proposed mathematical models, including Python libraries.

Competences to be developed by students:

C1 – Ability to model complex management decision problems using linear and integer linear programming.

C2 – Ability to efficiently use mathematical optimization tools to answer the proposed problems.

P1 – Linear and integer linear programming models

P2 – Computational tools for solving linear and integer linear models

   2.1 – Python tools

   2.2 – Other linear optimization tools

P3 – Economic interpretation of solutions: sensitivity analysis, parametric analysis and scenarios analysis

P4 – Prescriptive analysis applied to the management decision-making process

P5 – Applications of linear and integer linear programming to the decision-making process

   5.1 – Investment planning and project selection with cash flows

   5.2 – Production management

   5.3 – Financial management

   5.4 – Project planning

Students will be assessed by an assignment project carried out during the course and a final written test. The assignment is optional, while the written test is compulsory. The final mark will be equal to 50% of the mark for the assignment, plus 50% of the mark for the written test. The work grade will only be considered if the student obtains a minimum mark of 7.5 in the written test (on a scale of 0 to 20). If the student chooses not to do the assignment, their final grade will be determined entirely by the written test.

The following assessment methodologies (MA) will be used:

MA1 - Final exam.