## 6 Discussions and Conclusions  
AGV projects involve significant investment costs. Firms tend to use low-cost AGVs to decrease the cost, but the scheduling and efficiency of the system may be low in the case of a lack of a central management system. Therefore, in this study, to overcome this situation, AGV pools were designed to decrease the required AGV number and to obtain the minimum required AGVs by increasing the utilization of AGVs through AGV pools to meet the demand in manufacturing. The main objective of this research was to determine the optimum number of AGVs for decentralized AGV systems using the genetic algorithm (GA) and to estimate the required number of AGVs in the case of system parameter changes using an artificial neural network (ANN) and AGV pools with a reduced computational cost and time. The main problems faced in the scheduling of a PLC or card-based and lane-guided decentralized systems are mostly due to excessive experimental times, limited data, and the complexity, since the variables increase exponentially with the number of AGV circuits, reducing the utility of the model in complex applications. Therefore, in this study, a GA and an ANN are proposed to determine AGV numbers in order to overcome the shortcomings caused by the exponential increase in the variables with the number of AGV circuits, the excessive experimental time, limited data, and the complexity. In the first stage, a mixed integer model based on a time window analysis was developed to determine the optimum fleet size for low-cost AGVs with AGV pools. This approach successfully obtained optimum solutions for low numbers of AGV circuits, but a high computational time reduces the practicality of the model, especially in the case of complex systems. Therefore, a GA-based method was presented to handle this shortcoming, since Gas are an effective method in combinatorial optimization. In this study, an ANN is proposed to predict the effects of the system parameters on the AGV number and to check the possibility of reducing the AGV number under changing production conditions. In the present case, the ANN obtained a solution at a good rate with reduced computational costs, time, and correction errors compared to the GA at 0.4% (ANN), 7% (RSM), and 24% (MLR), and within a 2 min computational time compared to 20 min for the simulation-based analysis. The results showed that the ANN provides solutions which can be used in workshops with a reduced computational cost and time. It can be concluded that there is no need to repeat the GA optimization approach or simulation process to achieve the minimum AGV fleet size, especially in case of production demand changes. It was demonstrated that AGVs and pool combinations did not lead to any loss in production due to the lack of available AGVs. It was shown that the proposed approach provides a fleet size which requires five fewer AGVs, with a 29% reduction in the number of AGVs. The effects of system parameter changes were also investigated with artificial neural networks (ANNs) to estimate the required AGVs in the case of production requirement changes. The system designer could reduce the required AGV number by changing the system design and finding the most effective parameter using the proposed neural-network-based algorithm. It was observed that the trolley capacity is the most effective parameter. The speed and distance are the second and third most influential factors on the required number of AGVs. In this study, the speed of AGVs was assumed as 20 m/min and an increasing velocity could have a negative effect on AGV system safety as well as AGV system reliability. Thisplays an essential role in evaluating the processes and defining the operating strategy according to different production requirement conditions. Although several studies have been conducted on the design and control of AGV systems, they were mostly based on computer-assisted high-cost AGV systems.