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To facilitate a comprehensive evaluation of the novelty and relevance of the four publications in this cumulative dissertation, the following sections present a detailed overview of the knowledge bases for Facility Layout Problems and Reinforcement Learning. The elements described in Sect. 2.1 are essential for understanding the design requirements for solving FLPs. The subsequent Sect. 2.2 then explores the solution dimension for the thesis, which is vital for translating FLP concepts into the RL domain.

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Theoretical Background for Automated Layout Planning Using Reinforcement Learning

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  • 发表日期：

    2026年

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  • Benjamin Heinbach

摘要

To facilitate a comprehensive evaluation of the novelty and relevance of the four publications in this cumulative dissertation, the following sections present a detailed overview of the knowledge bases for Facility Layout Problems and Reinforcement Learning. The elements described in Sect. 2.1 are essential for understanding the design requirements for solving FLPs. The subsequent Sect. 2.2 then explores the solution dimension for the thesis, which is vital for translating FLP concepts into the RL domain.