Autonomous factories require high levels of adaptability, flexibility, and resilience to react to uncertainties on the shop floor, such as machine downtime. This paper proposes a negotiation-based, partial rescheduling method, combined with an existing multi-agent system, to swap jobs between machines. The negotiations are restricted to machines within the same work center, giving rise to a partial reschedule. A learning algorithm is also utilized, allowing machines to individually learn how to evaluate proposed bids from other machines and adapt the bids to their current environment. The main objective is to minimize the mean weighted tardiness of all jobs. Computational results indicate an improvement of 10–30 tardiness, compared to continuous rescheduling and complete rescheduling methods. In addition, a decrease of 70–80 sensitivity analysis and analysis of the partial reschedule.

中文翻译：

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增强在线机加工车间调度的稳定性和鲁棒性：工业 4.0 中处理机器停机的多代理系统和基于协商的方法

自动化工厂需要高水平的适应性、灵活性和弹性，以应对车间的不确定性，例如机器停机。本文提出了一种基于协商的部分重新调度方法，结合现有的多代理系统，在机器之间交换作业。谈判仅限于同一工作中心内的机器，导致部分重新安排。还利用了学习算法，允许机器单独学习如何评估其他机器提出的出价并使出价适应当前环境。主要目标是最大限度地减少所有工作的平均加权迟到率。计算结果表明，与连续重新安排和完全重新安排方法相比，迟到率提高了 10-30 倍。此外，敏感性分析和部分重新安排的分析减少了70-80。