Value Function Based Production Scheduling
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimizing Production Manufacturing Using Reinforcement Learning
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Multi-Machine Scheduling - A Multi-Agent Learning Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
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Finding optimal solutions for job shop scheduling problems requires high computational effort, especially under consideration of uncertainty and frequent replanning. In contrast to computational solutions, domain experts are often able to derive good local dispatching heuristics by looking at typical problem instances. They can be efficiently applied by looking at few relevant features. However, these rules are usually not optimal, especially in complex decision situations. Here we describe an approach that tries to combine both worlds. A neural network based agent autonomously optimizes its local dispatching policy with respect to a global optimization goal, defined for the overall plant. On two benchmark scheduling problems, we show both learning and generalization abilities of the proposed approach.