Publikation
Utilizing Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Under Sustainable Viewpoints
Jens Popper; William Motsch; Alexander David; Teresa Petzsche; Martin Ruskowski (Hrsg.)
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, located at 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, October 7-8, Belle Mare, Mauritius, IEEE, 2021.
Zusammenfassung
Current trends place great demands on the
flexibility and sustainability of modern production facilities. The
optimisation of these Flexible Job Shop Scheduling Problems
(FJSSP) under multiple objective variables, such as the makespan
or the consumed energy, is a great challenge for today’s planning
systems due to the constantly changing constraints. In this paper,
we present a method for multi-criteria dynamic planning of
production facilities under both common and sustainable target
variables, based on a Multi-Agent Reinforcement Learning
(MARL) procedure. This is experimentally applied to a planning
problem in a series of trials and compared with common methods.
Finally, the results and further research questions are presented.