Automatic Guided Vehicles (AGVs) are material handling equipment traveling on a network guide path. AGVs can be interfaced with other equipment for production and storage. Increasing the efficiency of automatic guided vehicle (AGV) scheduling is one of the important issues to improve the productivity of manufacturing enterprises.
Ri Il Chol, a researcher at the Faculty of Automation Engineering, has established a multi-objective mathematical model for scheduling multi-load AGVs carrying production materials and cutting tool consumables.
The objective function includes three objectives: the total moving distance of AGVs, the standard deviation of AGV workload and the standard deviation of the difference between the latest delivery time and the predicted time of tasks. Then, he implemented the assignment and ordering of tasks performed by AGV by implementing neighborhood search strategy using an improved harmony search algorithm.
He has applied the proposed harmony search algorithm to a virtual manufacturing enterprise to evaluate its performance.
The computational results show that the proposed harmony search algorithm outperforms the genetic algorithm.