Teaching Methodologies
The course adopts a pedagogy based on active learning focused on solving real-world problems. The integration of theory, practice, and
project work ensures effective skill consolidation.
The theoretical component is addressed through dialogic exposition, with concept presentation followed by discussions of real cases. This
approach ensures understanding of mathematical fundamentals without overwhelming students with excessive formalism, maintaining a
focus on applicability.
The practical component is the central axis of learning, following a “learning by doing” methodology. Students work with computational
tools, using free software such as GLPK, OpenSolver, and DEA-Solver to ensure continuity of learning outside the classroom. Sessions
include demonstrations by the instructor, followed by supervised practice where students replicate examples and solve progressively more
complex exercises.
The development of an applied project throughout the semester enables integrated consolidation of competencies. Students select a real
problem, apply the learned methodologies, and communicate results in a format suitable for decision-makers. This project is supported
through individual tutorials and feedback sessions.
Formative assessment is promoted through weekly non-graded exercises, allowing progress monitoring and identification of comprehension
difficulties, adjusting pedagogical intervention accordingly.
The combination of theoretical exposition, intensive computational practice, problem-solving, and project development ensures students
consolidate technical knowledge, critical thinking, and decision-oriented communication skills.
Learning Results
On completion students should be able to:
O1. Grasp fundamentals of linear and integer programming and interpret solutions for decisions.
O2. Apply DEA for relative efficiency assessment and benchmarking.
O3. Formulate and solve simple optimization problems in planning, production and performance evaluation.
O4. Implement models using accessible computational tools and interpret outputs.
O5. Communicate quantitative results clearly to decision-makers.
Teaching mixes concise theory, guided practice and a short applied mini-project. practice, and project
Program
1.Fundamentals of Linear Programming and Interpretation
1.1Fundamental concepts:variables,objective function,constraints
1.2Formulation of LP and ILP models and applications
1.3Duality,shadow prices,sensitivity analysis
1.4Applications transportation,assignment,and planning
2Computational Tools
21ntroduction to solvers(GLPK,OpenSolver/Pyomo)
2.2Data preparation,modeling,result visualization
2.3Reproducibility(files,scripts)
3Data Envelopment Analysis
3.1Fundamentals: relative efficiency,efficient frontier,advantages and limitations
3.2Classical models: CCR(CRS),BCC(VRS)and interpretation of scores
3.3Practical implementation/benchmarking
3.4Practice: case implementation(using DEA-Solver/DEAP),input/output selection,outlier identification,interpretation and
benchmarking(simplified case)
4Multicriteria Decision-Making and Communication
4.1Pareto concepts and weighted-sum method
4.2Communication of results and model limitations
5Guided practical project and presentation
Internship(s)
NAO
Bibliography
Hillier, F. S., & Lieberman, G. J. (2014). Introduction to operations research (10th ed.). McGraw-Hill Education.
Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (3rd ed.).
Springer.
Camanho, A. S., & Dinverno, G. (2023). Data envelopment analysis: A review and synthesis. In F. L. Nebot, J. M. Merigó, & C. L. Tan
(Eds.), Advances in efficiency and productivity analysis (pp. 33-54). Springer.
Emrouznejad, A., Petridis, K., & Charles, V. (2024). Data envelopment analysis with GAMS: A handbook on productivity analysis and
performance measurement. Springer.
Steuer, R. E. (1986). Multiple criteria optimization: Theory, computation, and application. John Wiley & Sons.
Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (Eds.). (1994). Data envelopment analysis: Theory, methodology, and
application. Kluwer Academic Publishers