Automated guided vehicle (AGV) selection, a key concern in manufacturing environments, is a complex, difficult task and requires extensive technical knowledge with systematic analysis. It is crucial to justify the selected equipment before actual implementation. Sawant, Mohite, and Patil presented a logical procedure to select automated guided vehicles in manufacturing environments. The procedure is based on the preference selection index (PSI) method. An automated guided vehicle selection index is proposed to evaluate and rank automated guided vehicles. While far from exhaustive and ignoring the bias introduced by AGV vendors, the published document (ISBN: 978-3-642-20208-7) offers some useful tools.

"Vehicle zone control" can be effectively achieved when each AGV communicates with each other without the need for a central zone controller. These data are shared by radio frequency communication to other vehicles in the area. When the information is received, each AGV, based on location, calculates whether or not a given zone can or cannot be entered.  This feature and functionality can be discussed and evaluated prior to the selection and implementation of any automation solution.

Modeling and Simulation in Engineering examined the pick up-dispatching and delivery-dispatching of a multiple-load AGV system.  The research concluded that by mixing different pick up-dispatching rules, several control strategies have been generated and the best control strategy determined. Some important criteria should be considered, such as System Throughput (ST), Mean Flow Time of Parts (MFTP), Mean Tardiness of Parts (MFTP), AGV Idle Time (AGVIT), AGV Travel Full (AGVTF), AGV Travel Empty (AGVTE), AGV Load Time (AGVLT), AGV Unload Time (AGVUT), Mean Queue Length (MQL), and Mean Queue Waiting (MQW). Several simulation experiments were based on a flow path layout to determine optimal results. A similar approach was used for determining the optimal fleet size.  While these findings are methodologically sound, the plant floor goals and objectives must be carefully considered and evaluated before considering these data conclusive.

Integrating a voice order selection system may consist of an AGV built on a standard pallet truck platform and directed by laser navigation.  Some of these AGV solutions are offered as an automatic solution for order picking, order putting, and transportation that improves accuracy and productivity by eliminating non-productive pre- and post-pick tasks. Some of these vehicles dock with the charger, absorb the necessary charge at a high rate, and automatically return to tasks. Versatile, the vehicle can work autonomously or can also be driven as a manual truck. Single, double, or triple pallet truck configurations may be specified.  The broader question should be asked if the voice picking solution is preferred and if pick-to-light (PTL) solutions are more efficacious.  Recent research suggests that picking rate accuracy may be best in the PTL environment if fast moving goods are involved.

Productivity must drive the rationale for AGVs.  The value of AGVs is the ability to navigate automatically and operate without drivers. As part of customers' production and intra-logistics, AGVs must improve the performance of production and warehousing. Through automation, impressive cost savings can be achieved, especially in shift work and in longer transfers. Personnel devotes more time to demanding tasks instead of monotonous, routine transfers. Automation increases the safety of work premises.  To quantify these productivity improvements and reduction in repetitive labor tasks, a benchmarking of the current state is vital.  Since AGV selection has to be justified with a rapid ROI (return-on-investment), only this type of pre-AGV data collection will provide the post-purchase data required to demonstrate the value proposition.