中国机械工程 ›› 2015, Vol. 26 ›› Issue (2): 247-244.

• 智能制造 • 上一篇    下一篇

基于改进蚁群算法的带时间窗废品收集车辆路径问题

刘琼;刘秀城;张超勇;饶运清   

  1. 华中科技大学数字制造装备与技术国家重点实验室,武汉,430074
  • 出版日期:2015-01-25 发布日期:2015-01-23
  • 基金资助:
    国家自然科学基金资助重点项目(51035001);国家自然科学基金资助项目(51275190);国家科技重大专项(2011ZX04015-011-07);中央高校基本科研业务费专项资金资助项目(HUST:2013ZZGH002) 

Waste Collection Vehicle Routing Problem with Time  Windows Based  on Improved  Ant  Colony  Optimization

Liu Qiong;Liu Xiucheng;Zhang Chaoyong;Rao Yunqing   

  1. State Key Laboratory of Digital Manufacturing Equipment &  Technology,Huazhong University of Science and  Technology,Wuhan,430074
  • Online:2015-01-25 Published:2015-01-23
  • Supported by:
    National Natural Science Foundation of China(No. 51035001, 51275190)National Science and Technology Major Project(No. 2011ZX04015-011-07);Fundamental Research Funds for the Central Universities(No. HUST:2013ZZGH002)

摘要:

建立了以最小化燃油消耗为优化目标的带时间窗、司机休息时间以及多个中转处理中心的废品收集车辆路径问题模型。提出了一种改进最大最小蚁群算法,针对时间窗特点,设计了两类满足时间窗约束的动态候选列表以提高算法的搜索效率。在最大最小蚁群算法的概率状态转移规则中引入了带距离限制的最近邻域搜索。10个基准实例中的9个实例比当前文献的最优解更好,从而验证了该模型和算法的可行性和有效性。

关键词: 大规模带时间窗车辆, 路径问题, 蚁群算法, 燃油消耗

Abstract:

A mathematical model aiming at minimizing the fuel consumption for the waste collection vehicle routing problem with time windows,driver rest period and multiple disposal facilities was set up.The main factors to affect the fuel consumption of a vehicle considered  herein were  the load of  a  vehicle and distance traveled.An improved MAX-MIN ant  system algorithm was proposed.Based  on characteristics of the time windows,two  kinds of dynamic candidate lists were designed to improve the searching efficiency of the algorithm.A new probabilistic condition transition rule for the MAX-MIN ant system algorithm was proposed.The nearest neighborhood search with distance limitation was integrated in the transition rule of proposed algorithm.The proposed model and algorithm were validated by comparion with  benchmark problems in literatures.

Key words: large scale vehicle with time windows, routing problem, ant colony optimization, fuel consumption

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