孙军艳,牛亚儒,苏宝,张媛媛.双区型仓库动态拣货策略的设计及路径优化研究[J].包装工程,2018,39(23):1-8.
SUN Jun-yan,NIU Ya-ru,SU Bao,ZHANG Yuan-yuan.Dynamic Picking Strategy Design and Path Optimization of Two-block Warehouse[J].Packaging Engineering,2018,39(23):1-8. |
| 双区型仓库动态拣货策略的设计及路径优化研究 |
| Dynamic Picking Strategy Design and Path Optimization of Two-block Warehouse |
| 投稿时间：2018-09-11 修订日期：2018-12-10 |
| DOI：10.19554/j.cnki.1001-3563.2018.23.001 |
| 中文关键词: 双区型仓库 动态拣货 路径优化 混合遗传模拟退火算法 |
| 英文关键词: two-block warehouse dynamic picking path optimization hybrid genetic simulated annealing algorithm |
| 基金项目:国家自然科学基金（51275407, 51475363, 11072192）；陕西省工业科技攻关项目（2018GY-026）；陕西科技大学博士科研启动基金（2018BJ-12）；国家级大学生创新创业训练计划（12145） |
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| 中文摘要: |
| 目的 针对双区型仓库，以拣货时间最短为目标函数构建数学模型，进一步提高拣货效率。方法 提出并设计动态货位调整与人工拣货协同作业的动态拣货策略，分别采用GA算法和GASA算法进行最优化求解。结果 GASA算法优于GA算法，拣货单为1张情况下的拣货时间可减少4%；与静态拣货策略相比，拣货单为10张情况下，采用GASA算法时，文中设计动态拣货策略下的拣货时间可减少6%，且随着拣货单数量的增加，拣货时间节约占比越大。结论 GASA算法较GA算法其求解动态拣货路径优化问题更高效、优化结果更好。文中所提动态拣货策略更方便实施，在静态拣货路径优化基础上，可进一步提高拣货效率，且拣货单越多，效果就越显著。 |
| 英文摘要: |
| The work aims to construct the mathematical model for the two-block warehouse with the shortest picking time as the objective function to further improve the picking efficiency. A dynamic picking strategy for dynamic cargo space adjustment and manual picking was proposed and designed. The GA algorithm and the GASA algorithm were respectively used to optimize the solution. The GASA algorithm was better than the GA algorithm. The picking time could be reduced by 4% in the case of one picking order. Compared with the static picking strategy, in the case of 10 picking orders, when the GASA algorithm was used, the picking time under the dynamic picking strategy designed herein could be reduced by 6%, and as the number of picking orders increased, more picking time would be saved. The GASA algorithm is more efficient and better optimized than the GA algorithm for solving the problem of dynamic picking path optimization. The proposed dynamic picking strategy can be implemented more conveniently. On the basis of static picking path optimization, the picking efficiency can be further improved, and the more the picking orders, the more significant the effect. |
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