Research on scheduling strategy for automated storage and retrieval system

Abstract

Abstract With the continuous and rapid growth of transport demand, scheduling strategy of warehouse has become a key issue in the field of logistics transportation. The structural differences of the warehouse, the automated storage and retrieval system (AS/RS) model and the two‐end dual stackers scheduling model (TDSM) are considered, and a new improved genetic algorithm (NIGA) is proposed. It can adjust the algorithm structure according to the density of population fitness value, and effectively optimize the stacker path. In the TDSM, an improved anti‐collision principle is proposed to avoid collision of two stackers. Besides, combined with the optimal anti‐collision boundary inspection mechanism, the best working area for the two stackers is allocated by using NIGA. Finally, the new improved GA is compared with GA and the adaptive GA on specific storage and retrieval tasks. The simulation results show that the proposed NIGA well outperforms other GAs in most instances, which indicates that it is an effective approach for the AS/RS and the TDSM scheduling optimization problem.

Keywords

• artificial intelligence
• mathematical computing
• trajectory control