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Deep Reinforcement Learning (DRL) has demonstrated operational excellence in several production-related problems. This paper applies DRL to facility layout problems (FLP) using Proximal Policy Optimisation, Advantage Actor-Critic and Deep Q-Networks. We show that the proposed approach produces an improved arrangement of facilities.

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Publication III: Deep Reinforcement Learning for Layout Planning—An MDP-Based Approach for the Facility Layout Problem

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  • 发表日期：

    2026年

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  • Benjamin Heinbach

摘要

Deep Reinforcement Learning (DRL) has demonstrated operational excellence in several production-related problems. This paper applies DRL to facility layout problems (FLP) using Proximal Policy Optimisation, Advantage Actor-Critic and Deep Q-Networks. We show that the proposed approach produces an improved arrangement of facilities.