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

摘要：

为提高跨层穿梭车系统料箱拣选出库效率，降低任务出库超时率，建立了跨层穿梭车双提升机系统出库任务调度数学模型，并将任务出库期限引入调度策略。在此基础上，使用蚁群-粒子群双层智能优化算法对模型进行了求解，引入随机变异对粒子群算法进行改进，提出使用置换复杂度对粒子变异程度进行控制，避免算法早熟收敛。利用MATLAB进行过程仿真，获得各调度方案的出库总时间和任务超时信息。通过实验证明该策略能更好地适应电商环境下复杂的出库任务调度要求，得到更为合理的任务调度方案。

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