Base Knowledge
It is desirable that they have knowledge of the basic concepts of linear programming.
Teaching Methodologies
The course involves an interdisciplinary approach that combines advanced mathematical concepts with principles of management and decision-making.
The chosen pedagogical model aims to actively engage students, providing meaningful and applicable learning. Following this model, the objectives are:
– A combination of lectures, computer-supported classes using modeling software, and classroom discussions analyzing problems.
– Providing constructive feedback throughout the learning process, encouraging reflection on mathematical approaches, and offering guidance for continuous improvement.
– Group assignments to solve problems or projects related to performance evaluation, promoting teamwork and the practical application of learned concepts.
– Frequently posing questions to stimulate student participation, as well as assigning problems to be completed outside of class for the purpose of assessing and monitoring knowledge acquisition.
Learning Results
Objectives:
1. Acquire theoretical and practical knowledge about various approaches and techniques used in performance evaluation.
2. Develop the ability to identify and select performance indicators relevant to different organizational contexts.
3. Learn to critically analyze existing performance evaluation methodologies, considering their limitations and potentials.
4. Empower students for the practical application of the Data Envelopment Analysis (DEA) methodology to assess the performance of organizational units.
Competencies:
1. Develop skills in analyzing performance data to extract valuable insights and inform decisions.
2. Cultivate the ability to discuss and communicate performance evaluation results effectively to different stakeholders.
3. Know how to use performance evaluation methodologies and be able to apply them to different sectors and/or organizations.
Program
1. Performance indicators and measures
1.1. Perspectives and Characterization of a Balanced Score Card
2. Introduction to Multicriteria Linear Programming
3. Classic DEA models
3.1. CCR
3.2. BCC
3.3 Non-oriented model of returns to scale (Additive)
4. Benchmarking
5. Rankings
6. Practical cases using computer support
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
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