计算机集成制造系统 ›› 2018, Vol. 24 ›› Issue (第9): 2349-2356.DOI: 10.13196/j.cims.2018.09.023

• 当期目次 • 上一篇    下一篇

子母式穿梭车仓储系统复合作业路径优化

杨玮,岳婷+,李国栋,王婷,刘江   

  1. 陕西科技大学机电工程学院
  • 出版日期:2018-09-30 发布日期:2018-09-30
  • 基金资助:
    国家自然科学基金重大资助项目(71390331);陕西省农业科技创新与攻关资助项目(2014K01-29-01);陕西科技大学科研启动基金资助项目(BJ12-21)。

Routing optimization of compound operations in shuttle-carrier warehousing system

  • Online:2018-09-30 Published:2018-09-30
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.71390331),the  Agricultural Science and Technology Innovation and Key Project of Shaanxi Province,China(No.2014K01-29-01),and the Research Foundation of Shaanxi University of Science and Technology,China(No.BJ12-21).

摘要: 针对子母式穿梭车仓储系统复合作业路径优化问题,分析了两套子母车配一台升降机在一次存取货作业中的3种不同作业方式,以存取货作业时间最短为目标建立了进出库复合作业调度模型。设计结合交叉变异算子的自适应粒子群算法对该模型进行路径优化求解,并对两套子母穿梭车进行合理调度。仿真结果表明,该调度模型针对子母式穿梭车仓储系统复合作业具有较高的准确性,升降机与子母穿梭车以1∶2的配比方式在不同订单规模下能够更经济有效地缩短仓储系统的进出库复合作业时间,提高作业效率。

关键词: 子母式穿梭车仓储系统, 复合作业, 调度模型, 路径优化, 自适应粒子群算法

Abstract: Aiming at the routing optimization problem of compound operations in a shuttle-carrier warehousing system,three kinds of different operating modes in a way of  matching two set of shuttle carriers with an elevator were analyzed in a couple of access operation.By taking the shortest time of storage and retrieval operations as the objective function,a loading/unloading scheduling model of compound operations was established.An adaptive particle swarm optimization algorithm combined with crossover and mutation operator was developed to solve the routing model,and two set of shuttle carriers were scheduled appropriately.The simulation results showed that the model had high accuracy for the compound operations of shuttle-carrier warehousing system,and the time of compound operations was shorten effectively and economically by matching two set of shuttle carriers with an elevator in different order scales for the shuttle-carrier warehousing system.

Key words: shuttle-carrier warehousing system, compound operations, scheduling modeling, routing optimization, adaptive particle swarm optimization

中图分类号: