## 2 Literature Review
### JSP with Limited Transport Resources  
Compared with the assumption of unlimited transport resources, the assumption of limited transport resources is more in line with real-world production environments and has therefore attracted increasing attention from researchers. [PERSON] et al. [16] studied the FJSP involving transport time and a limited number of robots, and proposed a clustering-based hybrid metaheuristic multi-agent model to minimize makespan. [PERSON] et al. [12] pointed out that, unlike traditional JSPs, the target workstation for subsequent operations in FJSP is not predetermined, and each transport device has a fixed capacity. They proposed two constraint programming-based methods to minimize makespan. [PERSON] et al. [13], [PERSON] et al. [14], and [PERSON] et al. [23] improved traditional genetic algorithms to solve the FJSP under limited transport resources with the objective of minimizing makespan. [PERSON] et al. [38] further refined the modeling of the transport process by considering four states of AGVs: idle, unloaded transport, loaded transport, and return transport. They proposed an improved genetic algorithm validated through a digital twin system, enhancing the practical feasibility of the scheduling results. [PERSON] et al. [25] addressed the high complexity and NP-hard nature of FJSP under limited transport resources by proposing a learning-based multi-population evolutionary optimization algorithm to tackle large-scale and complex instances. [PERSON] et al. [17] investigated a dynamic FJSP scenario with limited transport resources, aiming to minimize both makespan and total energy consumption. They incorporated disruptions such as machine breakdowns and proposed a hybrid deep Q-network (DQN) algorithm. [PERSON] et al. [39] presented a memetic algorithm combined with DQN for solving energy-aware FJSPs involving multiple AGVs, also targeting makespan and energy consumption minimization. [PERSON] et al. [40] addressed the often-overlooked assumption of infinite buffer capacity in previous research. They analyzed the impact of buffer size on job waiting time and makespan, and further explored scheduling optimization in zero buffer capacity settings using a memetic algorithm. [PERSON] et al. [41] also applied a DQN algorithm to solve multi-AGV FJSPs with objectives of minimizing makespan, total energy consumption, and their combination. Furthermore, [PERSON] et al. [11] introduced a novel nested hierarchical reinforcement learning framework that coordinates \"production agents\" and \"logistics agents\" to simultaneously minimize makespan and energy consumption. This framework also considers AGV breakdowns, a factor rarely addressed in prior studies.  
In summary, under the constraint of limited transport resources, the FJSP commonly aims to minimize makespan and total energy consumption. Solution approaches include linear programming, heuristic algorithms, and machine learning methods. Most studies consider AGVs as the primary transport resource. Although some have accounted for AGV status, energy consumption, and failure events, few have considered AGV charging behavior, and most assume unlimited battery capacity--an unrealistic simplification. Additionally, most studies assume a fixed number of AGVs, ignoring the real-world variability in AGV availability, which can lead to scheduling solutions that are impractical in actual production environments. In contrast, this study further refines the load modeling by, for the first time, jointly considering AGV transportation status, charging constraints, speed degradation, and dynamic variation in AGV quantity, thereby enhancing the model's adaptability and scalability to real industrial scenarios. A detailed literature comparison is shown in Table 1.