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

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

Abstract

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

CLC Number:

TP30

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.

References

[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.