陈俭新, 黄予洛, 宁蒙, 等. 某型自动化立体仓库储位优化算法研究[J]. 中国舰船研究, 2022, 17(4): 155–163. doi: 10.19693/j.issn.1673-3185.02374
引用本文: 陈俭新, 黄予洛, 宁蒙, 等. 某型自动化立体仓库储位优化算法研究[J]. 中国舰船研究, 2022, 17(4): 155–163. doi: 10.19693/j.issn.1673-3185.02374
CHEN J X, HUANG Y L, NING M, et al. Slotting optimization algorithm for automated 3D warehouse[J]. Chinese Journal of Ship Research, 2022, 17(4): 155–163. doi: 10.19693/j.issn.1673-3185.02374
Citation: CHEN J X, HUANG Y L, NING M, et al. Slotting optimization algorithm for automated 3D warehouse[J]. Chinese Journal of Ship Research, 2022, 17(4): 155–163. doi: 10.19693/j.issn.1673-3185.02374

某型自动化立体仓库储位优化算法研究

Slotting optimization algorithm for automated 3D warehouse

  • 摘要:
      目的  某船用自动化立体仓库在使用过程中存在不同类储具乱序的问题 该现象将影响后续物资保障效率,因此需要进行自动化立体仓库储位优化研究。
      方法  首先,分析某型自动化立体仓库的运行特点,以最小化储位优化时间、同组货品距离为目标建立储位优化模型;随后,为了克服传统蒙特卡洛搜索易陷入局部最优的缺点,引入模拟退火算法进行最优节点选择优化,同时改进蒙特卡洛树搜索算法;最后,对改进的蒙特卡洛树搜索算法进行算法优化性、稳定性和收敛性测试。
      结果  试验表明,与基于贪心、基于魔方还原以及传统蒙特卡洛树搜索算法相比,改进的蒙特卡洛树搜索算法在储位优化运行时间上至少优化30%。
      结论  通过在蒙特卡洛树搜索算法中加入最优路径随机选择因素,能够避免算法陷入局部最优;优化后的蒙特卡洛树搜索算法能够有效实现储位优化

     

    Abstract:
      Objectives  The disorder of different types of slotting equipment occurs during the use of an automated 3D warehouse, affecting the efficiency of subsequent material support. Therefore, it is necessary to carry out research on the slotting optimization of automated 3D warehouses.
      Methods  First, a slotting optimization model is established to minimize the inventory time and distance of the same group of goods after analyzing the operation characteristics of the automated 3D warehouse. Next, in order to overcome the shortcomings of the traditional Monte Carlo Tree Search (MCTS) algorithm that tends to fall into local optimality, a simulated annealing algorithm is integrated with the MCTS algorithm to optimize the node selection operation. Finally, the improved MCTS algorithm is tested for its optimization, stability and convergence.
      Results  The improved MCTS algorithm is at least 30% more optimized than the greedy, Rubik's cube reduction and traditional MCTS algorithms in terms of slotting optimization running time.
      Conclusions  By adding the optimal path random selection factor to the MCTS algorithm, it can be prevented from falling into local optimality, effectively achieving slotting optimization.

     

/

返回文章
返回