## 5 Experiments
### Instance Design and Hyperparameter Setting  
This section utilizes a public benchmark dataset proposed by [PERSON] [2], which includes 10 instances in total (MK01-MK10). To ensure that the proposed method can be evaluated under varying levels of scheduling complexity, this study designs three shop floor layouts by integrating practical manufacturing system scales and benchmark testing considerations. Specifically, the small-scale layout (6 machines) and medium-scale layout (10 machines) reflect typical shop floor sizes found in small to medium-sized manufacturing enterprises, while the large-scale layout (15 machines) is used to simulate more complex large-scale industrial production environments. The ratio of charging stations to machines (approximately one charging station per 5 to 6 machines) is based on commonly observed industrial configurations, aiming to balance AGV availability and mitigate the risk of charging station congestion. Furthermore, the use of varying layout scales facilitates a progressive increase in the decision space of the scheduling problem, thereby enabling a systematic assessment of the proposed scheduling method's scalability and robustness. These layouts are illustrated in Figure 6, reflecting a progressive increase in shop-floor complexity. Each dataset is assigned to its most suitable layout based on its size and processing requirements, as summarized in Table 4. The other parameters used in this paper are set as follows: the initial power of the AGV is a full power of 43,200, the AGV charging power is 1440, the load power is 1200, the no-load power is 600, and the distance between the two locations is equal to the AGV transporting time, and since the FJSP time is dimensionless, the dimensionless data is also used here along with the uniformity of the time scale. For OC, the charging time set in this paper is 10, and for MBC, the time set in this paper is 60. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Benchmarks** & **Workpiece** & **Machine** & **Charger** & **Layout** \\ \hline MK01–MK02 & 10 & 6 & 2 & 1 \\ MK03–MK04 & 15 & 8 & 2 & 2 \\ MK05 & 15 & 4 & 2 & 1 \\ MK06 & 10 & 15 & 3 & 3 \\ MK07 & 20 & 5 & 2 & 1 \\ MK08–MK09 & 20 & 10 & 2 & 2 \\ MK10 & 20 & 15 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Specific information about the dataset. The proposed algorithm, CRGPPO-TKL, is built on the PPO framework and adopts an Actor-Critic dual-network architecture. The architecture and hyperparameter settings are as follows: The Actor network consists of three fully connected layers that map the input state (including machine status, AGV battery levels, and task queues) to a joint action space. The Tanh activation function is employed to ensure gradient stability, and Softmax is used to generate the policy distribution. The Critic network shares the first two layers of the Actor and outputs the value estimate of the current state. The parameter settings in this study are based on a combination of established practices in the PPO literature and empirical tuning results specific to the FJSPLSP-LTCC problem. The discount factor \(\gamma\), GAE coefficient \(\lambda_{\mathrm{GAE}}\), entropy regularization coefficient \(\beta_{\mathrm{entropy}}\), and learning rates \(\eta_{\mathrm{actor}}\) and \(\eta_{\mathrm{critic}}\) are selected according to commonly adopted configurations in prior PPO studies [52]. For the newly introduced parameters in the  
Figure 6: Three types of workshop layouts. (a) Workshop layout 1 with 6 machines and 2 chargers. (b) Workshop layout 2 with 10 machines and 2 chargers. (c) Workshop layout 3 with 15 machines and 3 chargers.