The course is made up of ten two-hour lectures, which will be held once or twice a week. More details on the topics of each lecture can be found here below.

PART 1
Multi-objective optimization: fundamentals and classical algorithms (A. Artoni, 10 hrs)

• Introduction to multi-objective optimization
• Basic solution methods
• Advanced solution methods
• Simulation- and experiment-based multi-objective optimization
• Practical solution of real multi-objective optimization problems

Multi-objective optimization: genetic algorithms (F. Pistolesi, 10 hrs)

• Background on multi-objective genetic algorithms
• The Non-dominated Sorting Genetic Algorithm II (NSGA-II)
• Handling constraints
• Performance evaluation
• Multi-criteria decision making