Nowadays, automated guided vehicles (AGV) play very important role in modern factory automations. The AGV systems provide efficient routing for material flow and distribution among workstations at the exact time and place. Routing of AGV is the process of determining routes to fulfill their respective transport jobs. The routing problem of AGV in production system can be studied as a shortest path problem on a transfer network. It aims to find the shortest path between two vertices or nods. Most transport systems using AGV are centrally controlled and use static routing (pre-defined routes), which follows fixed line. Instead of using fixed path, dynamic routing for AGV can be used to add a high flexibility to the system. To accommodate the increased flexibility and reduced time, new operational controllers using Labview software must be able to adapt to small deviations. In this paper, routing of AGV, different AGV shortest path algorithms such as Dijkstra algorithm, A-star algorithm, Short Path Problem with Time Window (SPPTW), hybrid partial swarm optimization and genetic algorithm PSO-GA are discussed with highlights of their main differences and comparison between them. AGV Sch
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