With the development of intelligent manufacturing, robots are being increasingly applied in manufacturing systems due to their high flexibility. To avoid production disruptions caused by robot failures, higher requirements are imposed on the resilience of systems, specifically in terms of resistance, response, and recovery capabilities. In response to this, this paper investigates the resilient scheduling framework for multi-robot multi-station welding flow shop, thereby endowing and enhancing the resilience of the system. Within the resilient scheduling framework, a proactive scheduling method maximizing resistance capability is firstly proposed based on an improved NSGA-III with variable neighborhood search. Secondly, to improve the response and recovery capabilities of the system, a recovery scheduling method is presented. Therein, an adaptive trigger policy based on deep reinforcement learning is introduced to enhance the rapid response capability for disturbances, while the recovery optimization grants the system the ability to recover its performance that has been degraded due to the impact of disturbances. Finally, through simulation experiments and case study, it is verified that the proposed algorithms and framework possess superior performance of multi-objective optimization, which can endow the multi-robot multi-station welding flow shop with resilience to against uncertain robot failures.

中文翻译：

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针对机器人故障的多机器人多工位焊接流水车间调度的弹性调度框架


随着智能制造的发展，机器人以其高度的灵活性在制造系统中得到越来越多的应用。为了避免机器人故障导致生产中断，对系统的弹性，特别是抵抗、响应和恢复能力提出了更高的要求。针对这一问题，本文研究了多机器人多工位焊接流水车间弹性调度框架，从而赋予和增强系统的弹性。在弹性调度框架内，基于可变邻域搜索的改进NSGA-III，首先提出了一种最大化阻力能力的主动调度方法。其次，为了提高系统的响应和恢复能力，提出了恢复调度方法。其中，引入基于深度强化学习的自适应触发策略来增强对扰动的快速响应能力，而恢复优化则使系统能够恢复因扰动影响而降低的性能。最后，通过仿真实验和案例研究，验证了所提出的算法和框架具有优越的多目标优化性能，能够赋予多机器人多工位焊接流水车间针对不确定的机器人故障的恢复能力。