## 1 Introduction  
With the continuous advancement of technology, intelligent manufacturing has emerged as a key approach to improving production efficiency, optimizing resource allocation, and enhancing market competitiveness--thereby promoting high-quality development in the manufacturing industry. As a core component of intelligent manufacturing, the job shop scheduling problem (JSP) [1] plays a crucial role in optimizing resource allocation, shortening production cycles, and controlling manufacturing costs. Driven by increasingly customized and diversified production demands, flexible job shops have gained prominence. As an extension of traditional job shop scheduling, the flexible job shop scheduling problem (FJSP) allows operations to be assigned to multiple machines, each capable of processing different tasks [2]. This flexibility enables dynamic resource allocation and improves the shop's ability to handle uncertain order requirements. However, this increased flexibility also introduces new challenges, particularly frequent job transfers between machines, thereby giving rise to complex intra-shop transportation issues. Traditionally, production scheduling and logistics scheduling in FJSPs have been addressed independently, overlooking the intricate coupling between processing machines and transport equipment. As logistics technology evolves, AGVs have been widely adopted in manufacturing systems due to their high flexibility and operational stability. According to recent statistics, the global AGV market reached approximately USD 2.74 billion by the end of 2023, up from USD 1.86 billion in 2018, with a compound annual growth rate (CAGR) of 8.02%. In the same year, the global shipment of forklift AGVs--commonly used in manufacturing workshops--reached around 30,700 units, marking a year-over-year increase of 46.19% [3]. In real-world manufacturing scenarios, once an operation is completed on a machine, it must be transferred--often via AGVs--to the next designated machine or a storage buffer [4]. The subsequent operation can only commence once the processed part has been delivered to the area. This creates a strong interdependence between production scheduling and logistics scheduling. Therefore, production scheduling and logistics scheduling are intricately coupled in flexible job shops. However, most existing studies tend to ignore this coupling by investigating production scheduling [5; 6; 7] and logistics scheduling [3; 8; 9; 10] independently, which often results in infeasible scheduling solutions in real-world applications [11]. In recent years, some researchers have begun exploring collaborative scheduling approaches that jointly consider production and logistics in FJSP environments. Nonetheless, most of these studies assume either unlimited transportation resources [12; 13; 14], like a sufficient number of AGVs or mobile robots, or fixed transfer times [15]. A few works have attempted to address limited AGV availability by designing case-specific AGV configurations for different problem scales [16; 17]. Moreover, current research on the collaborative scheduling of production and logistics in flexible job shops is still in its infancy. Many practical factors have not been thoroughly addressed, such as the power consumption of AGVs, variations in transportation speed, charging strategies, and the availability of charging stations. In fact, such issues have already been well considered in other AGV-related application domains, such as port handling systems [18; 19; 20; 21]. To fill these research gaps, this study investigates a flexible job shop production-logistics collaborative scheduling problem with limited transportation resources and charging constraints (FJSPLSP-LTCC). On top of machine and operation selection, the proposed model integrates AGV assignment for each transport task, taking into account battery depletion, in-transit charging requirements, and charger constraints.