A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling

Riedmiller, Simone C.; Riedmiller, Martin A.

A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling

Simone C. Riedmiller, Martin A. Riedmiller

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Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 764-770.

Abstract

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. Th ey 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.

Simone C. Riedmiller, and Martin A. Riedmiller. 1999. A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99. IJCAI, 764–770. https://ijcai.org/proceedings/1999/109

@inproceedings{ijcai1999p109,
  title     = {A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling},
  author    = {Riedmiller, Simone C. and Riedmiller, Martin A.},
  booktitle = {Proceedings of the Sixteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-99}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Thomas Dean},
  pages     = {764--770},
  year      = {1999},
  note      = {Machine Learning},
  url       = {https://ijcai.org/proceedings/1999/109},
}

TY  - CPAPER
TI  - A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling
AU  - Riedmiller, Simone C.
AU  - Riedmiller, Martin A.
T2  - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1999
SP  - 764
EP  - 770
UR  - https://ijcai.org/proceedings/1999/109
ER  -