Dynamic reliability informed adaptive task scheduling for multirobot manufacturing system

Efficient and reliable scheduling is critical for multirobot manufacturing systems, yet existing performance diagnosis and reliability evaluation methods fail to capture the dynamic evolution of system states, and current scheduling often neglects reliability information. This paper proposes a collaborative scheduling method integrating dynamic reliability assessment. First, multisource operational signals are collected and fused to identify degradation stages. Based on a classification probability mapping mechanism, the service state is then converted into probabilistic attributes applicable to modeling and scheduling. Subsequently, a system model incorporating structure, state, and behavior is constructed, and reliability indicators of the system are dynamically evaluated through logical model evolution. On this basis, a heuristic multiagent reinforcement learning scheduling algorithm is designed, using reliability attributes and constraint graph structures as inputs to achieve collaborative scheduling of multiple robots. Finally, during task execution, real-time state changes are dynamically perceived, and the scheduling plan is adaptively updated by triggering rescheduling based on the real-time evaluation results, thus forming a reliability-informed closed-loop scheduling mechanism. Case studies demonstrate 18.2% reduction in task completion time for static allocation compared to four baseline methods, along with 17.1% improvement for dynamic rescheduling against engineering practices. These quantitative results confirm the method’s significant enhancements in scheduling responsiveness to degradation, adaptive task optimization, and overall system stability and efficiency.