Objectives and competences
The student will be able to:
• identify optimization problems and classify them according to their properties, with respect to possible solution methods
• formulate optimization problem as mathematical model
• choose and use the methods for solving different types of optimization problems
• use available software for solving optimization problems
Content (Syllabus outline)
1. Structure of optimization problem (recognizing the type, identifying the objective, decision alternatives, input parameters, and sources of uncertainty)
2. Develop a mathematical model to formalize the decision problem (the set of methods is adapted according to wishes and needs of participants):
o Advanced transportation, transshipment and allocation problems
o Advanced network models
o Inventory models (deterministic and stochastic approach)
o Decision analysis (deterministic and stochastic approach, multicriteria decision making)
o Forecasting methods
o Advanced queuing models
o Simulation
3. Analysis of the model solution (robustness and sensitivity analysis, managerial interpretation of the model solution)
4. Use Microsoft Excel advanced functions and addons (e. g., Solver, Sensitivity Toolkit, Precision Tree, RiskOptimizer
Learning and teaching methods
• Lectures
• Tutorial
• e-Learning
• individual research work
Intended learning outcomes - knowledge and understanding
Knowledge and understanding of:
At the end of the course, the students will be able to:
• develop critical thinking skills necessary to solve optimization problems in practice
• analyse complex problems with appropriate software and spreadsheet modelling
Readings
1. Taylor, B. W., (2019): Introduction to Management Science, 13th Edition, Pearson.
2. Hillier, F. S., Lieberman, G.J. (2021). Introduction to Operations Research, 11th Edition, Boston, McGraw-Hill.
3. Eligius M. T. Hendrix, Boglárka G. -Tóth (2010). Introduction to nonlinear and global optimization. New York, Springer.
4. Beichelt, F. (2006). Stochastic processes in science, engineering and finance, Boca Raton : Chapman & Hall/CRC.
5. Koole, G. (2010). Optimization of business processes: An introduction to applied stochastic modeling, VU University Amsterdam, Department of Mathematics.
Prerequisits
Familiarity with the course Operational research
Additional information on implementation and assessment Research work (40 %)
The computational part of the exam (30 %)
The theoretical part of the exam (30 %)
Criteria for passing the exam:
at least 50% of the maximum score at each of three parts (research work, the computational part, and the theoretical part of the exam)
