Abstract
An existing concept for sequence planning in production planning and control was extended by a global decision instance based on neural networks. Therefore, information regarding the state of the production and available orders were normalized and analyzed by one agent. In contrast to a partially observable Markow Decision Problem one single agent was allowed and used to process all available information. Feasibility and problems were examined and compared with a concept for decentralized decisions. The implementation consists of two parts, which continuously interact with each other. One part is a simulation of a job shop, including multiple machines. The other parts tackle the Markow Decision Problem with the use of double Q reinforcement learning in order to estimate the best sequence at any given time. Later, problems due to scaling and comparisons to the usage of multiple agents are given.
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Dasbach, T., Olbort, J., Wenk, F., Ander, R. (2022). Sequencing Through a Global Decision Instance Based on a Neural Network. In: Canciglieri Junior, O., Noël, F., Rivest, L., Bouras, A. (eds) Product Lifecycle Management. Green and Blue Technologies to Support Smart and Sustainable Organizations. PLM 2021. IFIP Advances in Information and Communication Technology, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-030-94335-6_24
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DOI: https://doi.org/10.1007/978-3-030-94335-6_24
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