›› 2017, Vol. 30 ›› Issue (4): 68-.

• 论文 • 上一篇    下一篇

基于改进蚁群算法的公交疏散策略研究

赵惠光 1,何胜学 1,黄 清 2,向乐佳 3   

  1. (1.上海理工大学 管理学院,上海 200093;2.周口师范学院 新闻与传媒学院,河南 周口 466001;
    3.上海同技联合建设发展有限公司,上海 200092)
  • 出版日期:2017-04-15 发布日期:2017-04-11
  • 作者简介:赵惠光(1991-),男,硕士研究生。研究方向:系统工程等。 何胜学(1976-),男,博士,副教授,硕士生导师。研究方向:系统工程等。
  • 基金资助:

    国家自然科学基金资助项目(70672110);上海市(第三期)重点学科基金资助项目(S30504);上海市教委科技创新基金资助项目(10YS105);上海理工大学博士启动基金资助项目(1D-00-307005)

Study on the Strategy of Bus Evacuation Based on Improved ant Colony Optimization

ZHAO Huiguang 1,HE Shengxue 1,HUANG Qing 2,XIANG Lejia 3   

  1. (1.Business School,University of Shanghai for Science and Technology,Shanghai 200093,China;
    2. College of Journalism and Communications,Zhoukou Normal University,Zhoukou 466001,China;
    3. Shanghai Tongji United Construction Development Co.,Ltd , Shanghai 200092,China)
  • Online:2017-04-15 Published:2017-04-11

摘要:

针对无预警灾难发生后的大规模人群疏散问题,提出了应急救援的公交疏散策略,通过对动态的疏散路网和疏散者的时间加载离散化处理,采用时空网络和数值分析的方法以疏散总时间最短,伤亡人数最少建立公交疏散的非线性混合整数规划模型,并将整个公交疏散过程与改进的蚁群算法相结合,通过信息素全局更新对疏散模型进行求解,最后以一个简单的公交疏散网络作为算例来求解最优疏散路径。结果表明,通过多次计算不仅验证了改进蚁群算法的有效性,同时求解疏散路径的速度和质量也得到提高。

关键词: 非线性混合整数规划, 公交疏散, 蚁群算法, 应急救援

Abstract:

Aim at the evacuation problems of large-scale crowd after the disaster without warning, presents the strategy of bus evacuation during the emergency rescue, puts forward the dynamic evacuation network and time load processing discretization, adopt the methods of numerical analysis and time-space network to shortest the total evacuation time ,minimum the casualties, then establish the mixed integer nonlinear programming model of bus evacuation, combined the evacuation process with improved ant colony optimization, proposed global pheromone updating to resolving the evacuation model, finally,to achieve the optimal evacuation path in an ordinary bus evacuation network.the results verified the validity of the algorithm by many times calculation ,and improved the speed and quality of solving the evacuation path.

Key words: bus evacuation;mixed integer nonlinear programming;ant colony optimization;emergency rescue

中图分类号: 

  • U491,TP301