Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system

Authors:
Moonsoo Shin;Kwangyeol Ryu;Mooyoung Jung
Affiliations:
Department of Industrial and Management Engineering, Hanbat National University, San 16-1, Duckmyoung-dong, Yuseong-gu, Daejeon 305-719, Republic of Korea;Department of Industrial Engineering, Pusan National University, San 30, Jangjeon-dong, Geumjeong-gu, Busan 690-735, Republic of Korea;School of Technology Management, Ulsan National Institute of Science and Technology (UNIST), Banyeon-ri 100, Ulsan 689-798, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

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.