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.

翻译：在本研究中，我们提出生成式制造系统（GMS）作为一种新颖的方法，以有效管理和协调自主制造资产，从而增强其响应能力和灵活性，以应对广泛的生产目标和人类偏好。与传统显式建模不同，GMS利用生成式人工智能（包括扩散模型和ChatGPT）从设想未来中进行隐式学习，标志着从模型最优决策向训练-采样决策的转变。通过集成生成式人工智能，GMS能够通过与人类的交互式对话实现复杂决策，使制造资产生成多个高质量全局决策，并可根据人类反馈进行迭代优化。实证结果表明，GMS在系统弹性和应对不确定性的响应能力方面有显著提升，决策时间从秒级缩短至毫秒级。研究强调了所生成解决方案的内在创造性和多样性，通过无缝且持续的人机交互促进以人为中心的决策。