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Generalisation is a critical requirement for reinforcement learning (RL) models applied in production planning tasks such as facility layout optimisation. This study investigates whether a single trained RL agent can generate efficient layouts across a diverse set of facility layout problems without retraining. To achieve this, we develop a scalable modelling approach using fixed-size image-based state representation and a static, masked action space, enabling consistent input-output dimensions regardless of layout size.

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Publication IV: From Theory to Application: Investigating the Generalizability of Facility Layout Problems Using a Deep Reinforcement Learning Approach

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  • 发表日期：

    2026年

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  • Benjamin Heinbach

摘要

Generalisation is a critical requirement for reinforcement learning (RL) models applied in production planning tasks such as facility layout optimisation. This study investigates whether a single trained RL agent can generate efficient layouts across a diverse set of facility layout problems without retraining. To achieve this, we develop a scalable modelling approach using fixed-size image-based state representation and a static, masked action space, enabling consistent input-output dimensions regardless of layout size.