An Effective Hybrid Jellyfish Search Algorithm for Multi-AGVs Path Planning

This paper considers a path planning problem of multiple automated guided vehicles (AGVs) in a production workshop with many varieties and small batches. To meet the logistics needs of the workshop, multi-AGVs material distribution and finished product recovery are involved at multiple task points. To solve this problem, we first present a mathematical model to optimize the handling cost under the constraints of AGV load and task priority. Secondly, we propose a hybrid jellyfish search (HJS) algorithm, where Lévy flight (LF) and differential evolution (DE) are into the jellyfish search (JS) algorithm by leveraging their advantages. Finally, we conduct various experiments to verify the performance of the proposed algorithm. The results show that HJS is more effective than other multi-AGV path planning algorithms in the AGV transportation distance and the operation of workshops, respectively.