Cooperative Scheduling of Multi-SM/RGV in Intensive Storage Environment

The efficiency problem of cooperative scheduling of multiple stacking machines (SMs) and rail guided vehicles (RGVs) in a three-dimensional warehouse environment is solved and optimized by effectively combining a genetic algorithm and a newly proposed group optimization scheme. Firstly, a mathematical model of the entry/exit completion times of RGVs and SMs is established, and the task sequences of RGVs and SMs are calculated according to the entry/exit pairing principle when using the genetic algorithm. Secondly, in the process of realizing SMs/RGVs entry/exit cooperative scheduling using the genetic algorithm, the key decoding operator of the genetic algorithm is designed in detail. And the sequence of entry/exit tasks performed by RGVs and SMs as well as the start and end time of the tasks are determined by decoding, so that the total completion time of all entry/exit tasks is the shortest. Then, a grouping optimization scheme is proposed to achieve the effect of working cooperatively of multiple RGVs and avoiding collision.