Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
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- Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
- Jung, Mooyoung; Shin, Moonsoo; Ryu, Kwangyeol
- Continuous process; Dynamic organization; Fuzzy inference systems; Goal-oriented; Goal-regulation; Market dynamics; Network-based; Production planning; Regulation mechanisms; Reinforcement learning approach; Reinforcement signal; Simulation studies; Working mechanisms
- Issue Date
- PERGAMON-ELSEVIER SCIENCE LTD
- EXPERT SYSTEMS WITH APPLICATIONS, v.39, no.10, pp.8736 - 8743
- Up-to-date market dynamics has been forcing manufacturing systems to adapt quickly and continuously to the ever-changing environment. Self-evolution of manufacturing systems means a continuous process of adapting to the environment on the basis of autonomous goal-formation and goal-oriented dynamic organization. This paper proposes a goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation. Individual goals are regulated by a neural network-based fuzzy inference system, namely, a goal-regulation network (GRN) updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). The GEN approximates the compatibility of goals with current environmental situation. In this paper, a production planning problem is also examined by a simulation study in order to validate the proposed goal regulation mechanism.
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