基于改进智能水滴算法的动态车辆配送路径优化

doi:10.16182/j.issn1004731x.joss.19-0374

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (9): 1808-1817.doi: 10.16182/j.issn1004731x.joss.19-0374

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

基于改进智能水滴算法的动态车辆配送路径优化

范双南¹, 陈纪铭^2,*, 高为民², 王增凤¹

1.湖南交通工程学院电气与信息工程学院,湖南衡阳 421009;
2.湖南工学院计算机与信息科学学院,湖南衡阳 421002

收稿日期:2019-07-24 修回日期:2020-04-15 出版日期:2020-09-18 发布日期:2020-09-18
通讯作者: 陈纪铭(1978-),男,湖南东安,硕士,讲师,研究方向为人工智能。
作者简介:范双南(1971-),男,湖南邵阳,硕士,副教授,研究方向为算法与人工智能;高为民(1975-),男,湖南祁东,硕士,教授,研究方向为人工智能。
基金资助:
湖南省教育厅科学研究重点课题(16A063)

Dynamic Vehicle Distribution Path Optimization Based on Improved Intelligent Water Drop Algorithm

Fan Shuangnan¹, Chen Jiming^2,*, Gao Weimin², Wang Zengfeng¹

1. Department of electrical and Information Engineering, Hunan Institute of Traffic Engineering, Hengyang 421009, China;
2. School of Computer and Information Science, Hunan Institute of Technology, Hengyang 421002, China

Received:2019-07-24 Revised:2020-04-15 Online:2020-09-18 Published:2020-09-18

摘要/Abstract

摘要： 针对当前车辆配送过程中存在的配送路径不合理、配送效率低和需求不确定性等问题,提出一种基于改进智能水滴算法的动态车辆配送路径优化方法。构建软时间窗惩罚函数,考虑顾客对配送时间的要求,建立顾客满意度函数。综合车辆配送过程的车速、货损成本、惩罚成本、顾客满意度等特征,建立车辆路径优化模型。采用智能水滴算法对车辆路径优化模型进行求解,使用灰狼优化算法改善智能水滴算法的搜索能力,获取最优路径。实验结果表明该方法能够提供实时优化的路径,减少调配成本。

关键词: 动态车辆路径优化, 配送时效, 智能水滴算法, 灰狼优化

Abstract: In view of the unreasonable distribution path, low distribution efficiency and demand uncertainty in the current vehicle distribution process, a dynamic vehicle distribution path optimization method based on the improved intelligent drop algorithm is proposed. According to the customer's demand for delivery time, the soft time window penalty function and customer satisfaction function are constructed. Based on the characteristics of vehicle speed, damage cost, penalty cost and customer satisfaction of vehicle distribution, a vehicle path optimization model is established. The vehicle path optimization model is solved by the intelligent water droplet algorithm. The grey wolf optimization algorithm is used to improve the search ability of the intelligent water droplet algorithm to obtain optimization path. Experiment results show that the method can provide the real-time optimization path and reduce the allocation cost.

Key words: dynamic vehicle path optimization, delivery time, intelligent water droplet algorithm, gray wolf optimization

中图分类号:

TP30

范双南, 陈纪铭, 高为民, 王增凤. 基于改进智能水滴算法的动态车辆配送路径优化[J]. 系统仿真学报, 2020, 32(9): 1808-1817.

Fan Shuangnan, Chen Jiming, Gao Weimin, Wang Zengfeng. Dynamic Vehicle Distribution Path Optimization Based on Improved Intelligent Water Drop Algorithm[J]. Journal of System Simulation, 2020, 32(9): 1808-1817.

参考文献

[1] Zhang Y, Zhang G, Fierro R, et al.Force-Driven Traffic Simulation for a Future Connected Autonomous Vehicle-Enabled Smart Transportation System[J]. IEEE Transactions on Intelligent Transportation Systems (S1524-9050), 2018, 19(7): 2221-2233.
[2] Song L B.Research on Intelligent Path Planning Algorithm for Logistics Distribution Vehicles in Low-carbon Cities[J]. Journal of Advanced Oxidation Technologies (S1203-8407), 2018, 21(2): 602-609.
[3] Yin Y, Zhang H Z.Improved Hybrid Bat Algorithm for Vehicle Routing Problem of Perishable Fresh Goods[J]. Journal of Computer Applications (S0974-1925), 2017, 37(12): 3602-3607.
[4] 吴艳群, 董鹏. 供应链中车辆路径问题的改进模拟退火算法[J]. 计算机工程与应用, 2016, 19(12): 256-260.
Wu Yanqun, Dong Peng.An Improved Simulated Annealing Algorithm for Vehicle Routing Problem in Supply chain[J]. Computer Engineering and Application, 2016, 19(12): 256-260.
[5] Ma C, Hao W, He R, et al.Distribution Path Robust Optimization of Electric Vehicle with Multiple Distribution Centers[J]. PLoS ONE (S1932-6203), 2018, 13(3): 189-205.
[6] 张立毅, 王迎, 费腾. 混沌扰动模拟退火蚁群算法低碳物流路径优化[J]. 计算机工程与应用, 2017, 10(1): 63-68.
Zhang Liyi, Wang Ying, Fei Teng.Chaos Disturbance Simulated Annealing Ant Colony Algorithm for Low Carbon Logistics Path Optimization[J]. Computer Engineering and Application, 2017, 10(1): 63-68.
[7] Wang J, Ying Z, Yong W, et al.Multiobjective Vehicle Routing Problems with Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances and Algorithms[J]. IEEE Transactions on Cybernetics (S2168-2267), 2016, 46(3): 582-594.
[8] Guo M, Ma C, He Y.Study on the Routing Optimization of Pickup & Delivery Vehicles under Road Travel Time Uncertainty[J]. Western China Communications Science & Technology (S1673-4874), 2017, 25(12): 100-105.
[9] 叶威惠, 张飞舟. 真实路况下的快递配送路径优化研究[J]. 计算机工程与科学, 2017(8): 143-150.
Ye Weihui, Zhang Feizhou.Research on the Optimization of Express Delivery Distribution Path under Real Road Conditions[J]. Computer Engineering and Science, 2017(8): 143-150.
[10] 郭琪, 韦金银, 郭鹏祥. 考虑实时路况下的商砼配送路径优化研究[J]. 软件工程, 2019, 22(4): 33-36.
Guo Qi, Wei Jinyin, Guo Pengxiang.Research on the Optimization of Commercial Concrete Distribution Route Considering Real-time Traffic Conditions[J]. Software Engineering, 2019, 22(4): 33-36.
[11] Chai H, He R C, Ma C Q, et al.Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load[J]. Discrete Dynamics in Nature and Society (S1026-0226), 2017(1): 1-13.
[12] 王恒, 徐亚星, 王振锋. 基于道路状况的生鲜农产品配送路径优化[J]. 系统仿真学报, 2019, 31(1): 126-135.
Wang Heng, Xu Yaxing, Wang Zhenfeng.Optimization of Fresh Produce Distribution Routes Based on Road Conditions[J]. Journal of System Simulation, 2019, 31(1): 126-135.
[13] 葛显龙, 薛桂琴. 不确定环境下多品类共同配送路径优化[J]. 计算机工程与应用, 2019, 55(9): 270-276.
Ge Xianlong, Xue Guiqin.Optimization of Multi-category Common Distribution Path in Uncertain Environment[J]. Computer Engineering and Application, 2019, 55(9): 270-276.
[14] Sun M Q, Pang D Z.Vehicle Routing Optimisation Algorithm for Agricultural Products Logistics Distribution[J]. International Journal of Applied Decision Sciences (S1755-8077), 2017, 10(4): 327-334.
[15] Zhang Q, Xiong S Q.Routing Optimization of Emergency Grain Distribution Vehicles Using the Immune Ant Colony Optimization Algorithm[J]. Applied Soft Computing Journal (S1568-4946), 2018, 71(18): 917-925.
[16] Kayvanfar V, Zandieh M, Teymourian E.An Intelligent Water Drop Algorithm to Identical Parallel Machine Scheduling with Controllable Processing Times: A Just-in-time Approach[J]. Computational & Applied Mathematics (S0101-8205), 2017, 36(1): 159-184.
[17] Alijla B O, Lim C P, Wong L P, et al.An Ensemble of Intelligent Water Drop Algorithm for Feature Selection Optimization Problem[J]. Applied Soft Computing (S1568-4946), 2018, 65(12): 531-541.
[18] 王敏, 唐明珠. 一种新型非线性收敛因子的灰狼优化算法[J]. 计算机应用研究, 2016(12): 3648-3653.
Wang Min, Tang Mingzhu.A Gray Wolf Optimization Algorithm with a New Nonlinear Convergence Factor[J]. Computer Application Research, 2016(12): 3648-3653.
[19] Li Q, Chen H L, Huang H, et al.An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis[J]. Computational and Mathematical Methods in Medicine (S1748-670X), 2017, 20(5): 1-15.
[20] Emary E, Zawbaa H M, Grosan C.Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks[J]. IEEE Transactions on Neural Networks and Learning Systems (S2162-237X), 2018, 29(3): 681-694.

基于改进智能水滴算法的动态车辆配送路径优化

Dynamic Vehicle Distribution Path Optimization Based on Improved Intelligent Water Drop Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	赵文清, 覃智补. 采用改进型智能水滴算法的电力环境经济调度[J]. 系统仿真学报, 2018, 30(8): 3213-3218.
[2]	王田田, 王艳, 纪志成. 基于改进极限学习机的滚动轴承故障诊断[J]. 系统仿真学报, 2018, 30(11): 4413-4420.