# Multi-AGV Scheduling under Limited Buffer Capacity and Battery Charging Using Simulation Techniques
## 2 Literature Review  
Numerous researchers have employed different approaches to address AGV scheduling issues. Typically, AGV scheduling entails determining job selection rules for assigning specific transport goods to AGVs and AGV selection rules for choosing suitable AGVs from the available pool. These two key decisions, job selection and AGV selection, are fundamental to AGV scheduling and have been the subject of numerous prior studies [14; 15; 16; 17; 18]. However, early research in this field mainly focused on small-scale AGV operations in limited-sized workshops, and practical operational issues such as AGV battery charging and buffer capacity limitations were not considered. Meanwhile, in recent years, researchers have dedicated significant attention to the development of practical AGV scheduling methods applicable in real work environments. These studies propose scheduling models and optimization algorithms that consider real-world operational issues such as buffer capacity limitations and AGV battery charging. In real manufacturing environments, buffer capacity limitations play an important role in the efficiency and flexibility of the production process. Some physical manufacturing sites have limited buffer capacity for a variety of reasons, including the need to effectively manage work-in-process (WIP) and meet short lead times. Therefore, it is important to consider factors such as buffer capacity limitations in order to establish scheduling suitable for real environments. There is existing research that addresses the impact of buffer capacity limitations on job scheduling. [PERSON] et al. [19] discussed the issue of scheduling jobs for multiple multiload AGVs. The study considered the transportation time between these AGVs and the processing time between machines, addressing a situation where there is a limitation on the input buffer capacity of each machine. In a similar study to the previous work, the literature by [PERSON] et al. [20] also addressed the multi-load AGV scheduling problem due to limited buffer capacity. A heuristic algorithm based on simulated annealing was proposed to minimize the maximum completion time and utilize it in a real environment. [PERSON] et al. [21] proposed a mathematical model that integrates the scheduling of yard cranes and AGVs at a container terminal and solved the scheduling problem by reflecting new constraints in the model to consider limited buffer capacity and storage allocation of containers. Meanwhile, studies on the topic of AGV battery charging have also looked into issues like power management and energy efficiency. [PERSON] et al. (2020) developed a multi-objective nonlinear programming model based on a genetic algorithm to minimize the maximum completion time in a flexible manufacturing cell, considering the battery charging and waiting time of AGVs. Additionally, the influence of various loads on AGV power consumption was discussed. [PERSON] et al. (2020) focused on optimizing AGV scheduling in automated container terminals (ACTs) and specifically conducted research on scheduling strategies considering battery replacement and speed control. Additionally, they proposed a mixed integer programming (MIP) model designed to minimize carbon dioxide emissions and delays caused by AGV operation and developed a solution based on genetic algorithms. [PERSON] et al. (2020) addressed the AGV scheduling problem considering the battery status of the AGV to solve the problem of AGV utilization rate and high workload imbalance in ACTs and proposed a collaborative scheduling model based on simulated annealing (SA) and genetic algorithm (GA). [PERSON] et al. (2020) introduced a mixed integer linear programming (MILP) model and a heuristic algorithm as a solution to the AGV scheduling problem and proved that efficient scheduling considering AGV battery constraints is possible through experiments using data from the actual shopfloor. Meanwhile, [PERSON] et al.