This unit aims at providing knowledge and skills about principles, concepts and techniques of the following subfields of optimization: stochastic optimization, optimization under uncertainty, simulation-optimization, simheuristics, applications of optimization to real-life problems in the services and manufacturing industries (logistics, transportation, production, etc.).
- Recognize the importance of combinatorial stochastic optimization in the general context of optimization;
- Identify the main methods and techniques of simulation-optimization and simheuristics for optimization problems under scenarios with uncertainty;
- Apply simulation-optimization techniques to solve real-life stochastic optimization problems in the services and manufacturing industries.
1-Introduction
1.1-Exact methods for solving optimization problems under uncertainty;
1.2-Approximate methods for solving optimization problems under uncertainty;
2-Simulation-Optimization
2.1-Hybrid methods combining simulation with optimization;
2.2-Simheuristics (combining simulation with metaheuristics);
3- Applications to real-life optimization problems under uncertainty
3.1-Logistics & Transportation problems;
3.2- Manufacturing Problems.
- Spall: Introduction to Stochastic Search and Optimization, Wiley, 2003
- El-Ghazali Talbi: Metaheuristics: From Design to Implementation, Wiley 2009
- Faulin & Juan & Grasman, Fry (eds.): Decision Making in Service Industries: A Practical Approach, CRC Press
E-learning
Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.
