Using Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Problems
Jens Popper, Martin Ruskowski
Abstract
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.
Topics & Concepts
Reinforcement learningScheduling (production processes)Computer scienceJob shopJob shop schedulingExtensibilityIndustrial engineeringDistributed computingFlow shop schedulingEngineeringArtificial intelligenceOperations managementEmbedded systemOperating systemRouting (electronic design automation)Scheduling and Optimization AlgorithmsAssembly Line Balancing OptimizationAdvanced Manufacturing and Logistics Optimization