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Communication Dans Un Congrès Année : 2022

Sequencing Through a Global Decision Instance Based on a Neural Network

Résumé

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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hal-04186143 , version 1 (23-08-2023)

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Thomas Dasbach, Johannes Olbort, Felix Wenk, Reiner Ander. Sequencing Through a Global Decision Instance Based on a Neural Network. 18th IFIP International Conference on Product Lifecycle Management (PLM), Jul 2021, Curitiba, Brazil. pp.334-344, ⟨10.1007/978-3-030-94335-6_24⟩. ⟨hal-04186143⟩
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