Decision Support Techniques

Base Knowledge

There are no basic knowledge prerequisites other than those required for admission to this Master course.

Teaching Methodologies

The teaching activity follows on a presential regime, with the exposition of concepts, techniques and methods.

Learning Results

Objectives:
Today’s economic systems are strongly influenced by conflicting and usually contradictory interests and are characterised by multiple factors of a wider and almost always complex system. It is in this context that the manager makes its moves, being regularly asked to take decisions.

Mathematical modelling techniques directed to some optimization and decision problems are introduced, including project planning, project selection, financial management, production management.

Competences:

The student is expected to show ability to model, in the context of linear and integer linear programming, some decision problems. The student must also know techniques and computer support tools to solve this type of mathematical models.

Program

1 – Linear and integer linear programming models

   1.1 – Mathematical formulation of problems

   1.2 – Applications of mathematical modelling to Business Management

   1.3 – Economic analysis of the models

2 – Solving techniques for continuous linear and integer linear programming

   2.1 – Description of the most used methcontiunous and integer linear programming

   2.2 – Softwares for solving larger sized problems

3 – Economic interpretation of the solutions

   3.1 – Sensitivity analysis

   3.2 – Parametric analysis

4 – Applications of continuous and integer linear programming to the decision analysis process

   4.1 – Investments planning and project selection with cash-flows

   4.2 – Production management

   4.3 – Financial management

   4.4 – Project planning

   4.5 – Shift scheduling and rostering

Curricular Unit Teachers

Pedro João Coimbra Martins

Internship(s)

NAO

Bibliography

Essential bibliography:

– Martins, P. Teaching materials made available at the NONIO platform, Author’s Edition.

 

Supplementary bibliography:

– Cornuéjols, G., Peña, J., & Tütüncü, R. (2018). Optimization Methods in Finance. Cambridge University Press. doi:10.1017/9781107297340.

– Hillier, F. S., & Lieberman, G. J. (2021). Introduction to Operations Research, Mc Graw-Hill. ISBN: 978-0-071-13989-2

– Rardin, R.L. (2017), Optimization in Operations Research (2nd ed.), Pearson Higher Education, Hoboken. ISBN: 978-0-13-438455-9