Swarm-Consensus-Based Human-Machine-Logistics Collaborative Scheduling Method

In the human-centric Industry 5.0 environment, factors such as personalized customer demands and the fuzzy characteristics of task times in human-machine collaborative operations will be further considered, resulting in a type of human-machine-Iogistics collaborative scheduling problem for flexible job shops. This problem involves random arrival times of workpieces, fuzzy processing times, and stringent order deadlines. Such a multi-disturbance manufacturing scenario places higher demands on resource collaboration and scheduling within the workshop. To address this problem, this paper introduces swarm theory and constructs a human-machine-logistics resource task consensus model. A real-time task slackness model is developed based on personalized order characteristics, and a real-time scheduling method considering human-machine-logistics resource consensus is proposed, enabling adaptive real-time collaboration of swarm resources in smart workshops for randomly arriving orders. Finally, the effectiveness of the proposed method is evaluated through simulation case studies.