Abstract
The needs of flexibility, agility and adaptation capabilities for modern manufacturing systems increase constantly. In this paper, we propose an original approach combining active/intelligent product architecture with learning mechanism to assure flexibility and agility to the overall manufacturing system. Using learning approaches as Reinforcement Learning (RL) mechanism, an active product can be able to reuse learned experiences to enhance its decisional performances. A contextualization method is proposed to improve the decision making of the product for scheduling tasks. The approach is then applied to a case study using a multi-agent simulation platform.
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Bouazza, W., Sallez, Y., Aissani, N., Beldjilali, B. (2015). A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products. In: Borangiu, T., Thomas, A., Trentesaux, D. (eds) Service Orientation in Holonic and Multi-agent Manufacturing. Studies in Computational Intelligence, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-319-15159-5_22
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DOI: https://doi.org/10.1007/978-3-319-15159-5_22
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