To improve the operation efficiency of automated guided vehicle in the "parts-to-picker" warehouse, aiming at the three sub-problems of AGVs-pallets task assignment, single AGV path planning and multi-AGVs collision avoidance, this paper built a mathematical model with the objective of minimizing the driving distance of AGVs. Firstly, it designed the improved Hungarian algorithm to solve the matching results according to the characteristics of bipartite matching problem between AGVs and pallets. Secondly, it proposed an improved genetic algorithm(IGA) with two-dimensional encoding mechanism, for which it designed a local search operator replace the original mutation operation. It successfully applied the algorithm to the single AGV path planning problem on the premise of improving the search performance of this algorithm. Then, it utilized spatiotemporal data to design a three-dimensional mesh conflict detection method, and set the priority of AGVs according to the number of commodity SKUs to reduce the collision probability when multiple AGVs execute tasks. Finally, it studied the AGV path optimization for the two scenarios of no collision and considering collision at the warehouse of 32 m×22 m, which gave reasonable driving distance and collision times. IGA was compared with standard genetic algorithm. The results indicate that the IGA can obtain the higher quality solutions in a reasonable time, which reduced the driving distance by about 1.74% and the solution time of the algorithm by about 37.07%. Furthermore, for the sensitivity analysis on the number of AGVs, it tested the influence of different numbers of AGVs on the driving distance and the number of collisions under different target pallets sizes. It was found that the number of 14~16 AGVs is the optimal configuration. These results verify the feasibility of the model and the effectiveness of the proposed algorithm.

[2023-12-05] Accepted Paper /doc/2023.09.0426/v1

[2024-05-05] Printed Article /doc/2023.09.0426/v2