Neural and heuristic job allocation planner for AGVs

Abstract

Automated guided vehicles (AGVs) are automatic load carriers that transfer objects from one location to another in a factory environment. Due to the increasing complexity of factory floor environments coupled with the need for increased flexibility in AGV systems, it is becoming increasingly important to be able to dynamically alter both the AGV job queue and the AGV path. In this paper, a new method based on an artificial neural network model is presented for evaluating the best job assignment so as to achieve better system efficiency.<>