A Neural Network-Based Algorithm with Genetic Training for a Combined Job and Energy Management for AGVs

Abstract

Automated guided vehicles are designed for internal material transport in production and warehouse environments. To do this, transport orders must be assigned to the vehicles. In addition, the vehicles often have an electric drive. The batteries required for this are discharged during operation. Therefore, it must be decided when the vehicles must go to a charging station. This control option is often ignored and the vehicles are only sent for loading when the battery has (almost) completely discharged. In this work, a procedure that simultaneously solves the assignment of jobs and the decision when a vehicle should drive to a charging station is presented and evaluated. It is based on neural networks trained by genetic algorithms. The evaluation shows that the presented method delivers better results than a method that combines the "First-Come-First-Served" and the "Nearest-Vehicle-First" methods and in which the charging processes are controlled by a fixed battery threshold.