大量需求点下基于深度Q学习的受损路网抢修队调度
作者:
作者单位:

合肥工业大学计算机与信息学院

作者简介:

通讯作者:

中图分类号:

TP181

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Repair crew scheduling for the damaged road network with enormous demand points using deep Q-learning
Author:
Affiliation:

School of Computer Science and Information Engineering, Hefei University of Technology

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    受损路网抢修是重特大自然灾害发生后开展应急处置和救援的一个基本前提,主要研究如何对道路抢修队进行合理的调度以快速恢复路网畅通、保障救援队伍和应急物资从出救点及时输送到各需求点.然而,已有研究在面向大量需求点时往往很难给出有效的调度策略.为此,本文首先基于路网模型和马尔科夫决策过程分析抢修队修复受损路网的关键因素,并设计一种双反馈回报函数,然后基于深度Q学习求解抢修队的最优调度策略.对比实验结果表明,在大量需求点环境下,本文所提方法具有很好的稳定性和可靠性,兼顾了受损路网的修复效率和运输效率,能够以更少的修复代价让所有需求点可达,为灾后复杂应急场景下的受损路网抢修提供了一个有益的尝试.

    Abstract:

    Repairing the damaged road network, which mainly focuses on how to reasonably schedule the repair crew to quickly unblock the road network and ensure that rescue teams and emergency resources in the source node can be delivered to different demand nodes in time, is a basic premise for emergency disposal and rescue after the occurrence of extraordinarily serious natural disasters. However, it is difficult for the existing methods to find a feasible scheduling strategy under a large number of demand nodes. To this end, in this paper, the key factors of repairing the damaged road network are first analyzed according to the road network model and Markov decision-making process, based on which a double-feedback reward function is designed. Then, the deep Q-learning is utilized to solve the optimal scheduling strategy of the repair crew. Finally, the comparative experimental results demonstrate that under enormous demand nodes, the proposed approach has good stability and reliability, considers both the repair and transportation efficiencies of the damaged road network, can make all the demand nodes accessible with less repair cost, and provides a useful attempt to repair the damaged road network in complex emergency scenarios of post-disaster.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-01-21
  • 最后修改日期:2021-08-27
  • 录用日期:2021-09-10
  • 在线发布日期: 2021-10-01
  • 出版日期: