Abstract
In this study, we introduce Generative Manufacturing Systems (GMS) as a novel approach to effectively manage and coordinate autonomous manufacturing assets, thereby enhancing their responsiveness and flexibility to address a wide array of production objectives and human preferences. Deviating from traditional explicit modeling, GMS employs generative AI, including diffusion models and ChatGPT, for implicit learning from envisioned futures, marking a shift from a model-optimum to a training-sampling decision-making. Through the integration of generative AI, GMS enables complex decision-making through interactive dialogue with humans, allowing manufacturing assets to generate multiple high-quality global decisions that can be iteratively refined based on human feedback. Empirical findings showcase GMS's substantial improvement in system resilience and responsiveness to uncertainties, with decision times reduced from seconds to milliseconds. The study underscores the inherent creativity and diversity in the generated solutions, facilitating human-centric decision-making through seamless and continuous human-machine interactions.
Abstract (translated)
在这项研究中，我们提出了生成制造系统（GMS）作为一种新颖的方法来有效地管理和协调自主制造资产，从而提高其对生产各种目标及人类偏好的响应能力和灵活性。与传统显式建模不同，GMS采用生成式AI，包括扩散模型和ChatGPT，进行从预见到的未来进行隐式学习，标志着从模型最优到训练抽样的决策转变。通过集成生成式AI，GMS使通过与人类交互进行复杂决策成为可能，允许制造资产生成多个高质量的全球决策，并可以根据人类反馈进行迭代改进。实证研究展示了GMS在系统弹性和对不确定性的改进，决策时间从秒减少到毫秒。该研究突出了生成式解决方案固有的创造力和多样性，通过无缝连续的人机交互促进人本决策。
URL
https://arxiv.org/abs/2405.00958
