[1]靳文舟,邓钦原,郝小妮,等.改进人工蜂群算法的农村DRT路径优化研究[J].郑州大学学报(工学版),2021,42(04):84-90.
　JIN Wenzhou,DENG Qinyuan,HAO Xiaoni,et al.Research on Route Optimization of Rural DRT Based on Improved ABC Algorithm[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):84-90.

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改进人工蜂群算法的农村DRT路径优化研究()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

42

期数:

2021年04期

页码:

84-90

栏目:

出版日期:

2021-07-30

文章信息/Info

Title:

Research on Route Optimization of Rural DRT Based on Improved ABC Algorithm

作者:

靳文舟; 邓钦原; 郝小妮; 朱子轩

华南理工大学 土木与交通学院，广东 广州 510641

Author(s):

JIN Wenzhou; DENG Qinyuan; HAO Xiaoni; ZHU Zixuan

School of Civil and Transportation，South China University of Technology，Guangzhou 510641，China

关键词:

需求响应公交; 农村居民出行; 车辆路径问题; 同时接送模式; 自适应大邻域人工蜂群算法

Keywords:

demand responsive transit; travel of rural residents; vehicle routing problem; simultaneous pick-up and drop off mode; adaptive large neighborhood search artificial bee colony algorithm

文献标志码:

A

摘要:

农村地区的需求响应公交模式因其特殊的运输特性区别于常规公交模式。本文根据农村客运出行的特殊规律和实际需求情况,对农村地区DRT模式进行了探讨，提出了考虑农村居民同时接送条件的车辆路径问题模型。在模型中考虑同时接送乘客的特殊时间窗和其他限制条件，构建了以运输网络总成本最优为目标的VRP模型，并提出了一种两阶段的自适应大邻域人工蜂群算法进行求解。最后通过实际算例仿真来验证该模型和算法的可行性。结果显示，农村地区需求响应公交模型在考虑同时接送的条件下更贴合实际情况，优化结果良好；另外，相比于遗传算法和ALNS算法，自适应大邻域人工蜂群算法收敛速度更快，成本均值、标准差和方差结果更优，在精度和鲁棒性上有较好的表现，可以有效地找到高质量解决方案。

Abstract:

The travel demand of residents in rural areas is low and scattered,which makes it difficult for conventional bus mode to sustain.According to the characteristics of rural residents′ travel demand,in order to reduce the operation cost and improve the transportation efficiency,a vehicle routing problem model considering the demand responsive transit (DRT)simultaneous pick-up and drop off mode in rural areas was constructed.And an improved two-stage adaptive large neighborhood search artificial bee colony algorithm was proposed to solve the model.The example results showed that in the rural areas with low demand density,the rural DRT simultaneous pick-up and drop off model was more economical and practical.Compared with the genetic algorithm and adaptive large neighborhood search algorithm,the average cost of the improved artificial bee colony algorithm was 9% and 3% lower than the above two algorithmsrespectively,and the convergence speed was faster,the performance was better in accuracy and stability,so it could effectively find the optimal solution with high quality.

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