## 5 Case Study
### Effectiveness Analysis of the Proposed Method
Similarly, the reductions in delay time were also remarkable, at 55.9%, 69.2%, and 41.0%, respectively. When considering workers' learning and forgetting effects, the performance of HCMLCS was further optimized. For small, medium, and large order sizes, completion time was further reduced by 5.20%, 6.35%, and 10.32%, while average delay time was reduced by 53.0%, 67.7%, and 70.7%, respectively. These improvements ensure efficient production processes, allowing enterprises to respond to customer demands more quickly, thereby enhancing customer satisfaction and loyalty. Moreover, while the reduction in energy consumption was relatively smaller, the overall optimization effect remains significant, demonstrating that the HCMLCS method exhibits strong flexibility and adaptability in dynamic production environments. The above analysis indicates that the HCMLCS algorithm demonstrates unique advantages compared to existing scheduling methods in the literature. Many traditional scheduling methods focus primarily on individual task scheduling, overlooking task collaboration, dynamic resource variations, and fluctuations in worker efficiency (such as learning and forgetting effects). For example, classical scheduling approaches such as Shortest Processing Time (SPT) and First-Come-First-Served (FCFS), while effective in certain scenarios, may lead to inefficient resource allocation, task delays, and reduced overall \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Without Learning and Forgetting Effects (NLF)**} & \multicolumn{6}{c}{**Considering Learning and Forgetting Effects (NNW)**} \\ \hline \multirow{3}{*}{**Algorithm**} & **Order** & **Makespan** & **Energy** & **Tardiness** & **Algorithm** & **Order** & **Makespan** & **Energy** & **Consumption** & **Tardiness** \\  & **Size** & **(s)** & **(KJ)** & **(s)** & **Algorithm** & **Size** & **(s)** & **(KJ)** & **(s)** \\ \hline \multirow{3}{*}{FCFS} & 20 & 3977 & 590,216 & 90 & \multirow{3}{*}{FCFS} & 20 & 3755 & 552,464 & 78 \\  & 40 & 7146 & 1,144,348 & 360 & & 40 & 6923 & 1,056,896 & 264 \\  & 60 & 11,158 & 1,751,944 & 1261 & & 60 & 10,180 & 1,543,460 & 695 \\  & 20 & 3703 & 575,264 & 62 & & 20 & 3826 & 550,168 & 29 \\  & 40 & 6980 & 1,129,000 & 293 & SPT+ & 40 & 6540 & 1,024,008 & 154 \\  & 60 & 10,066 & 1,677,836 & 638 & & 60 & 9061 & 1,477,712 & 277 \\  & 20 & 3851 & 577,984 & 95 & & LPT+ & 20 & 3461 & 503,136 & 47 \\  & 40 & 6893 & 1,128,296 & 227 & & 40 & 6104 & 1,002,832 & 69 \\  & 60 & 9818 & 1,668,320 & 829 & & 60 & 8365 & 1,453,240 & 313 \\  & 20 & 3912 & 586,904 & 49 & & 20 & 3736 & 548,532 & 28 \\  & 40 & 7508 & 1,147,084 & 396 & & SRP & 40 & 6695 & 1,034,884 & 61 \\  & 60 & 10,135 & 1,686,340 & 591 & & 60 & 8887 & 1,463,496 & 185 \\  & 20 & 3963 & 591,972 & 147 & & 20 & 3618 & 544,396 & 49 \\  & 40 & 7343 & 1,151,672 & 358 & & 40 & 6945 & 1,033,268 & 225 \\  & 60 & 10,493 & 1,695,692 & 816 & & 60 & 8748 & 1,455,264 & 432 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(Cont.\)production efficiency in multi-resource, high-disturbance production environments. Even when some traditional methods take worker learning and forgetting effects into account, their scheduling optimization results still fall short of the HCMLCS algorithm, further highlighting the limitations of conventional scheduling strategies in complex, high-dynamic production environments. Additionally, although the Order Insertion (OC) method from literature [35] performs well in logistics-intensive production settings, experimental results indicate that in high-disturbance, low-logistics-priority, and multi-resource collaborative production environments, the OC method exhibits lower adaptability and inferior scheduling performance compared to the HCMLCS algorithm.