文章摘要
吕东许,李少梅,周炤,马京振,温伯威.基于改进变邻域搜索算法的多批次协同任务规划[J].包装工程,2023,44(5):222-229.
LYU Dong-xu,LI Shao-mei,ZHOU Zhao,MA Jing-zhen,WEN Bo-wei.Multi-batch Collaborative Task Planning Based on Improved Variable Neighborhood Search Algorithm[J].Packaging Engineering,2023,44(5):222-229.
基于改进变邻域搜索算法的多批次协同任务规划
Multi-batch Collaborative Task Planning Based on Improved Variable Neighborhood Search Algorithm
  
DOI:10.19554/j.cnki.1001-3563.2023.05.028
中文关键词: 多批次协同任务  变邻域搜索  自适应邻域选择  任务分配  路径规划
英文关键词: multi-batch collaborative task  variable neighborhood search (VNS)  adaptive neighborhood selection  task assignments  path planning
基金项目:国家自然科学基金(42101454,42101455);河南省中原学者资助项目(202101510001);智慧中原地理信息技术河南省协同创新中心和时空感知与智能处理自然资源部重点实验室基金资助项目(212102)
作者单位
吕东许 信息工程大学郑州 450001 
李少梅 信息工程大学郑州 450001 
周炤 信息工程大学郑州 450001 
马京振 信息工程大学郑州 450001 
温伯威 信息工程大学郑州 450001 
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中文摘要:
      目的 对多批次协同任务进行分析与建模,并研究任务规划的求解算法。方法 以车载装备多批次协同执行任务为例,综合考虑时间协同、任务区域协同和补给区域协同约束,以暴露时间最短为目标函数建立模型,并提出一种改进变邻域搜索算法进行求解,该方法根据邻域的优化能力自动调整迭代时选择该邻域的概率。结果 仿真结果表明,改进策略在不降低最优解质量的情况下,能够避免标准变邻域搜索算法后期易出现某些邻域长时间无法寻找到最优解的情况,有效提高了算法的效率。结论 变邻域搜索算法可以解决多批次任务规划问题,改进后的算法减少了后期对优化能力不强的邻域的搜索次数,有效提升了算法效率。
英文摘要:
      The work aims to analyze and model multi-batch collaborative tasks, and to study the algorithm of task planning. In this work, vehicle-mounted equipment multi-batch collaborative task was analyzed and modeled. In the modeling process, the constraints of time collaboration, task area collaboration and replenishment area collaboration were integrated, and the minimum exposure time was taken as the objective function. The simulation results showed that the improved strategy could avoid the situation that some neighborhoods could not find the optimal solution for a long time in the later stage of the standard variable neighborhood search algorithm, and improve the efficiency of the algorithm without reducing the quality of the optimal solution. It is concluded that the variable neighborhood search algorithm can solve the multi-batch task planning problem. The improved algorithm reduces the search times of the neighborhood with weak optimization ability in the later stage, and effectively improves the efficiency of the algorithm.
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