Murdivien Shokhikha Amalana, Um Jumyung
Department of Industrial and Management System Engineering, Kyung Hee University, 1732 Deogyeong-daero, Yongin-si 17104, Republic of Korea.
Sensors (Basel). 2023 Aug 3;23(15):6928. doi: 10.3390/s23156928.
Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement Learning is a potential solution to solve more complex problems by introducing artificial neural networks in Reinforcement Learning. In this paper, a game engine was used for Deep Reinforcement Learning training, which allows visualization of view learning and result processes more intuitively than other tools, as well as a physical engine for a more realistic problem-solving environment. The present research demonstrates that a Deep Reinforcement Learning model can effectively address the real-time sequential 3D bin packing problem by utilizing a game engine to visualize the environment. The results indicate that this approach holds promise for tackling complex logistical challenges in dynamic settings.
制造系统需要具备弹性和自组织能力，以适应供应链变化中诸如产品变更或紧急订单等意外干扰，同时提高机器人化物流流程的自动化水平，以应对人类专家短缺的问题。深度强化学习是一种通过在强化学习中引入人工神经网络来解决更复杂问题的潜在解决方案。在本文中，使用了一个游戏引擎进行深度强化学习训练，它比其他工具更直观地允许对视图学习和结果过程进行可视化，还使用了一个物理引擎来提供更逼真的问题解决环境。本研究表明，深度强化学习模型可以通过利用游戏引擎可视化环境来有效解决实时顺序三维装箱问题。结果表明，这种方法在应对动态环境中的复杂物流挑战方面具有潜力。
