## 5 Case Study
### Analysis of the Impact of Learning and Forgetting Effects on Scheduling  
To further prove the impact  
According to the charts, across all order sizes, all scheduling methods that consider learning and forgetting effects show varying degrees of improvement in completion time, energy consumption, and average delay. The impact of workers' learning and forgetting rates on production scheduling is significant. Workers with higher learning abilities and lower forgetting rates (e.g., YEW and SW) can complete tasks in a shorter time with lower energy consumption and shorter delays. This indicates that they quickly adapt to tasks and execute them efficiently, thereby enhancing overall production efficiency. In contrast, workers with weaker learning abilities and higher forgetting rates (e.g., NNW) exhibit longer completion times, higher energy consumption, and greater delays, suggesting that a lower learning rate and a higher forgetting rate lead to lower efficiency and slower adaptation during task execution.  
As order size increases, the scheduling performance of all workers improves, especially for experienced workers (e.g., OEW and SW). This suggests that as order size grows, workers become more adaptable to tasks, particularly experienced workers, whose learning and adaptation processes are more efficient. However, it is important to note that although larger order quantities lead to greater optimization effects, this improvement is limited. Each worker's learning ability has an upper limit (parameter \(\theta\)), beyond which further improvements in scheduling performance will plateau.  
Although this study has validated the effectiveness of the HCMLCS algorithm through simulations, the experimental environment remains idealized and does not fully consider complex real-world factors such as equipment failures, material shortages, or other unexpected disruptions. Additionally, worker-to-worker variations were only preliminarily considered. Future research could enhance the robustness and adaptability of the algorithm by conducting field tests in more complex and uncertain production environments.  
Overall, the proposed method not only holds significant theoretical value in the field of production scheduling but also provides strong support for the practical application of intelligent manufacturing and scheduling optimization. From a theoretical perspective, this study makes an innovative contribution to the field of production scheduling, particularly in incorporating workers' learning and forgetting effects. By considering fluctuations in worker efficiency, this study not only optimizes scheduling performance but also introduces a novel research perspective for intelligent scheduling in dynamic production environments. This approach effectively addresses common challenges in production scheduling, such as inefficient resource allocation and task delays, thereby filling a gap in existing research. From a practical application perspective, the HCMLCS algorithm significantly enhances production efficiency, reduces delays, optimizes resource utilization, and enables enterprises to achieve more efficient resource scheduling in complex and dynamic production environments. The algorithm dynamically adjusts resource allocation based on changes in workers' learning curves, ensuring high execution efficiency across different order sizes,  
Figure 7: The results for three metrics of five worker types for Order 60: (**a**) completion time comparison chart; (**b**) energy consumption comparison chart; (**c**) delay time comparison chart.  
especially for large-scale orders. Additionally, by optimizing resource allocation and task assignment, enterprises can reduce operational costs, improve on-time delivery rates, and enhance customer satisfaction.