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Using Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Problems

Jens Popper; Martin Ruskowski
In: Roberto Teti; Doriana M. D'Addona (Hrsg.). CIRP Proceedings. CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME), Pages 63-67, Vol. 112, Special Issue 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 14-16 July 2019, Gulf of Naples, Italy, Elsevier B.V. 2021.


The flexibilization and increase of new production concepts such as matrix manufacturing with the help of autonomous logistics robots (AGVs) pose new challenges to production scheduling. To solve these flexible job shop scheduling problems (FJSSP) for arbitrary production arrangements, a concept for a multi-agent system based on Deep Reinforcement Learning (MARL) is proposed. The focus is on speed and quality of scheduling, easy creation of new manufacturing setups and extensibility to other scheduling problems such as logistics. An algorithm to solve these problems is given and evaluated on an exemplary job shop. Future research questions and extensions are then discussed.