## 2 Literature Review
[PERSON] developed and investigated a queuing model to estimate AGV fleet sizes; however, the model produced inferior solutions in light traffic [28]. Simulation techniques are frequently employed in the search for solutions to problems associated with AGVs [2]. [PERSON] et al. estimated the AGV fleet size in a simulation based on minimizing the total travel time and total cost. It was reported that the simulation method provided an accurate fleet sizing solution when compared to the analytical method [29]. [PERSON] et al. proposed a simulation model to control the AGVs. A methodology based on shortening the total operational manufacturing time was proposed [30]. In another approach, [PERSON] et al. reported that it could be difficult to assign an accurate number of AGVs with the mathematical model and it could take time to simulate the entire system. In the first stage, they estimated the required number of AGVs with the mathematical model and reduced the computation time, since they utilized the output as the simulation input. They reported that the fleet size estimation for multi-loaded AGVs with the heuristic method was not accurate when compared to real- life requirements; however, simulation methods led to a better solution [31]. [PERSON] analyzed AGV parameters with the surface response and simulation method to determine the optimal parameters to minimize the required number of AGVs [32]. [PERSON] et al. applied simulation optimization and data envelopment for multi-objective (minimum delivery time and maximum lot delivery) vehicle fleet sizing in an automated material handling system. The numerical study demonstrated that the proposed method provided accurate solutions [33]. [PERSON] et al. proposed a minimum-cost flow model for container terminals. A simplex algorithm and greedy search method were adopted to solve the model. They demonstrated that the time-constrained greedy tool search could complement the simplex algorithm, which requires further computation time. The AGV simplex model provided the optimal planning solution; however, as the number of work orders increased, the computation time forced the analyst to convert to the greedy algorithm. However, this algorithm could not always determine the optimum solution and got stuck at the local minimum [34]. [PERSON] et al. proposed a model that included a workshop scheduling problem and a conflict-free routing problem and utilized Ant Colony Optimization (ACO) to minimize the AGV production time and guide the AGVs to avoid collisions. A mathematical method and an ACO model were developed. It was suggested that the ACO model could reduce the computation time [35]. The same topic was studied by [PERSON] et al. (2007), who suggested the employment of a bilevel decomposition algorithm to solve simultaneous scheduling and conflict-free routing problems and minimize the total weighted tardiness of the task sets. Lagrangian relaxation reduced the computation time, which was an important issue in higher task counts [36]. The decomposition method has been frequently used in scheduling and routing. [PERSON] et al. developed an AGV routing solution with a Petri net by decomposing the problem into sub-problems that were solved with [PERSON]'s algorithm [37]. Several cut methods, as well as heuristic methods, were studied to solve sub-problems. [PERSON] et al. proposed an advanced particle swarm optimization algorithm for vehicle routing problems under uncertain customer demand. The novel method was reported to improve the solution quality and the algorithm was determined as adequate for large-scale problems [38]. Another study was conducted with robust vehicle routing optimization under uncertain demand by [PERSON] et al. The application of robust optimization in vehicle routing problems under uncertain demand reduced the unmet demand [39]. [PERSON] proposed the employment of a scheduler to assign AGVs and machines in the short term and a heuristic model was developed for this assignment.