## 6 Conclusions  
This paper, set against the backdrop of Industry 5.0, explores the issue of human-machine-logistics collaborative scheduling in intelligent workshops, highlighting the differences between intelligent workshops involving workers and those that do not. A human-machine-logistics collaborative scheduling framework was proposed, considering workers' learning and forgetting effects. This framework makes several key contributions:  
Introduction of a Learning and Forgetting Model: By incorporating a mathematical model for learning and forgetting, this paper addresses the scheduling challenges posed by variable worker efficiency. The model effectively captures the state changes in workers under the influence of learning and forgetting effects, providing reliable data for further scheduling optimization.  
Development of Real-Time State Models: The framework establishes real-time status models for human-machine-logistics resources, integrating dynamic information on workers, equipment, and materials within the workshop. This allows the scheduling system to self-organize based on real-time data, improving flexibility and response speed.  
Adaptive Scheduling Algorithm: An adaptive scheduling algorithm based on real-time worker efficiency is proposed. This algorithm dynamically adjusts scheduling strategies in real time according to worker states, ensuring production stability and high efficiency.  
Through case study validation, the proposed method demonstrated significant advantages in enhancing production efficiency, increasing scheduling system flexibility, and handling variations in worker states. The validation results showed that the scheduling framework considering workers' learning and forgetting effects could maintain stable system operation in complex and changing production environments.  
In conclusion, the method proposed in this paper provides an innovative and effective solution to intelligent workshop scheduling, fully reflecting the importance of human-centered manufacturing in the Industry 5.0 era. However, there is still room for further exploration. Some indicators did not show better performance in cases where learning speed was high, and forgetting speed was low, suggesting that the impact of workers' learning and forgetting effects on collaborative scheduling involves complex triggering mechanisms that could affect overall scheduling outcomes. Future research could delve deeper into these mechanisms. Additionally, combining intelligent algorithms, such as deep learning or genetic algorithms, with human-machine-logistics collaborative scheduling could further improve scheduling precision and efficiency, providing new solutions for complex, dynamic scheduling problems. This would create smarter management modes for coordinating workers, machines, and logistics and offer theoretical foundations and practical references for future research.  
[PERSON] proposed the key idea for this paper and wrote the manuscript; [PERSON] reviewed the manuscript and put forward constructive suggestions; [PERSON] and [PERSON] provided suggestions for organizing this manuscript and polished it; [PERSON] helped with reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.  
Key Scientific and Technological Research Projects in Henan Province (Nos. 252102221016, 252102220023, 242102221018). Henan Province University Science and Technology Innovation Talent Support Plan (Grant No.24 HASIT048).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).