Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0
提出一种基于协商的部分重调度方法，结合多智能体系统，通过机器间交换作业来应对停机，使平均加权延误降低10%-30%，同时减少70%-80%的敏感性。
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.