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A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products

Abstract : Modern manufacturing systems need to be increasingly flexible, agile and adaptable. In this paper, we propose an original approach combining ac-tive/intelligent product architectures with learning mechanisms to ensure flexi-bility and agility of the entire manufacturing system. Using learning approaches such as Reinforcement Learning (RL), an active product can reuse experience learned to enhance its decisional capability. A contextualization method is pro-posed to improve product decision making for scheduling tasks. The approach was applied to a case study using a multi-agent simulation platform.
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https://hal-uphf.archives-ouvertes.fr/hal-03404089
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Tuesday, October 26, 2021 - 2:25:53 PM
Last modification on : Tuesday, November 30, 2021 - 3:36:02 PM

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Wassim Bouazza, Yves Sallez, Nassima Aissani, Bouziane Beldjilali. A Model for Manufacturing Scheduling Optimization Through Learning Intelligent Products. Service Orientation in Holonic and Multi-agent Manufacturing, 594, Springer International Publishing, pp.233-241, 2015, Studies in Computational Intelligence, ⟨10.1007/978-3-319-15159-5_22⟩. ⟨hal-03404089⟩

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