关键词: 跨层穿梭车双提升机系统, 任务调度, 蚁群-粒子群双层智能优化算法, 随机变异

Abstract:

To improve the order picking efficiency of tier-to-tier shuttle-based storage and retrieval system, and to reduce the outbound task timeout rate, a mathematical model for the outbound task scheduling is established, and the task delivery deadline is introduced into the scheduling strategy. On this basis, the Max-Min Ant System-Discrete Particle Swarm Optimization（MMAS-DPSO）algorithm is used to solve the model. The random mutation is introduced to improve the performance of Particle Swarm Optimization（PSO） algorithm, the permutation complexity is used to control the particle variability and avoid the premature convergence of the algorithm. The MATLAB is used to perform the task outbound simulation, task outbound total time and timeout rate of each scheduling scheme are obtained. Finally, the experiments prove that the strategy can better adapt to the complex outbound task scheduling requirements in the e-commerce environment and get a more reasonable tasks scheduling scheme.

Key words: tier-to-tier shuttle-based storage and retrieval system with double lifts, task scheduling, Max-Min Ant System-Discrete Particle Swarm Optimization（MMAS-DPSO） algorithm, random mutation