A resilient scheduling framework for multi-robot multi-station welding flow shop scheduling against robot failures

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.