Teaching Methodologies
Students are requested to follow the classes onsite or online. Lecturing involves the exposition of concepts,techniques and methods, with a strong focus on practical applications. Software will be used to help answering largesized problems, mostly within the Python libraries.
The student will be graded by a project made within the course and by a written exam. The written exam is mandatory,while the project is optional. The final grade is equal to 50% of the project grade plus 50% of the written exam result.The project grade will only be considered if the student obtains a minimum result of 7.5 in the written exam (on a scaleof 0 to 20). If the student chooses not to carry out the project, its final grade will be entirely determined by the written exam.
Learning Results
This discipline introduces mathematical modeling techniques devoted for solving complex problems within planningand management, resorting to linear programming and linear integer programming mathematical models.
The discipline is mainly devoted to solving applied problems, focusing on: investment planning, productionmanagement, financial management, project planning and shift scheduling and rostering.
Given the scale of the problems involved, computational means of optimization will be adopted for the resolution of theproposed mathematical models. These means rely mainly on Python libraries, seeking an easy use of the data and theresults of the proposed problems.
It is intended that the student can model optimization problems within the aforementioned themes, using linear andlinear integer programming. The student should also be able to handle the data from these problems and exploit theresults generated, using Python tools.
Program
1 – Linear and linear integer programming models. Economic analysis
2 – Computational tools for solving linear and linear integer programming models
2.1 –Python tools
2.2 – Other linear optimization tools
3 – Economic discussion of the solutions. Sensitivity analysis and parametric analysis
4 – Data processing and results processing using Python tools
5 – Selected decision making problems:
5.1 – Investment planning
5.2 – Production management
5.3 – Financial management
5.4 – Project planning
5.5 – Shift scheduling and rostering
Internship(s)
NAO
Bibliography
Cornuejols, G., & Tütüncü, R. (2006). Optimization methods in finance (Vol. 5). Cambridge U Press.
Hart, W. E., Laird, C. D., Watson, J. P., Woodruff, D. L., Hackebeil, G. A., Nicholson, B. L., & Siirola, J. D. (2017). Pyomo-optimization modeling in python (Vol. 67). Berlin: Springer.
Hillier, F. S., & Lieberman, G. J. (1995). Introduction to operations research. McGraw-Hill Science, Engineering &Mathematics.
Rardin, R.L. (1998). Optimization in Operations Research, Prentice Hall, New Jersey.
Cornuejols, G., & Tütüncü, R. (2006). Optimization methods in finance (Vol. 5). Cambridge U Press.
Hart, W. E., Laird, C. D., Watson, J. P., Woodruff, D. L., Hackebeil, G. A., Nicholson, B. L., & Siirola, J. D. (2017). Pyomo-optimization modeling in python (Vol. 67). Berlin: Springer.
Hillier, F. S., & Lieberman, G. J. (1995). Introduction to operations research. McGraw-Hill Science, Engineering &Mathematics.
Rardin, R.L. (1998). Optimization in Operations Research, Prentice Hall, N Jersey.