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

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

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  • NETL
  • NSTL
  • Elsevier
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 multistation 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.

Resilient scheduling frameworkMulti-robot multi-station welding flow shopRobot failuresProactive schedulingRecovery scheduling

Ming Wang、Peng Zhang、Guoqing Zhang、Kexin Sun、Jie Zhang、Mengyu Jin

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College of Mechanical Engineering, Donghua University, Shanghai, China||Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China||Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China

Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China||Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China

Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Institute of Artificial Intelligence, Donghua University, Shanghai, China||Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China|| College of Information Science and Technology, Donghua University, Shanghai, China

2025

10.1016/j.rcim.2024.102835

Robotics and computer-integrated manufacturing

Robotics and computer-integrated manufacturing

SCI
ISSN：0736-5845
年,卷(期)：2025.91(Feb.)
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