Teaching genetic programming and reinforcement learning methods to graduate students

Course Description

The efficient use of production systems plays an essential role in all manufacturing industries. The resulting scheduling problems differ between production systems. Due to their combinatorial nature, they are often hard to solve. In this module, students learn to apply analytics to support scheduling decisions. Scheduling methods for application areas such as assembly lines, process industries, and the automotive industry are introduced. Moreover, we discuss concepts to deal with uncertainties in scheduling such as stochastic programming. We also introduce methodologies for real-time/online scheduling exploiting sensor/IoT data including Markov decision processes and machine learning techniques such as genetic programming and reinforcement learning

My Contribution

• Created and taught session about genetic programming
• Created and taught Session about reinforcement learning