Research on Scheduling Optimization of Air Cargo 3D Storage WCS System Based on Hybrid Genetic Algorithm

With the increasing cargo handling volume and higher time efficiency requirements in the air cargo industry, the operational efficiency and path planning of warehouse systems have become critical issues that need to be addressed. To improve the operation efficiency of stacker cranes and reduce empty load time and transport distance, this study uses a combination of GA algorithm, NSGA-II algorithm, and hybrid genetic algorithm for analysis. The aim of this study is to optimize the stacker crane's operation path by selecting the shortest path among multiple scheduling solutions while ensuring that tasks are completed according to their priority order to improve the overall warehouse operational efficiency. A hybrid genetic algorithm model was developed for the optimized scheduling of stacker crane operation paths, combining the global search capability of the GA algorithm with the multi-objective optimization features of the NSGA-II algorithm. The results show that the hybrid genetic algorithm performs best among the three algorithms, with the fastest convergence speed and the ability to find the optimal solution with fewer iterations, yielding a target function value of 294.17, outperforming both the GA algorithm and the NSGA-II algorithm. This study addresses the inefficiency of traditional algorithms in handling complex warehouse task scheduling and provides a new optimization approach to improve warehouse system operation efficiency.