Optimization of Unmanned Vehicle Delivery Routes Considering Charging and Time Windows

Authors

  • Zhiwei Liu
  • Di Liu
  • Song Liu

DOI:

https://doi.org/10.54097/tg21v812

Keywords:

Unmanned Vehicle, Time Windows, Charging Station, Genetic Algorithm

Abstract

With the increasing application of unmanned vehicles in logistics delivery, more and more researchers are paying attention to improving delivery efficiency, improving delivery service levels and reducing costs. This research considers the unmanned vehicle delivery problem with time windows, where the delivery must be completed within the required time window, otherwise there will be a penalty and a penalty cost will be incurred. And considering the limited range of the unmanned vehicle's battery, whether it needs to enter the charging station for charging or not, the model is established based on the minimum running cost and penalty cost. And a genetic algorithm is designed. Finally, the effectiveness of the model and algorithm is verified by examples.

Downloads

Download data is not yet available.

References

[1] Dantzig G, Ramser J.The truck dispatching problem[J]. Management Science, 1959,6( 1) : 80-91. DOI: https://doi.org/10.1287/mnsc.6.1.80

[2] Yong Wang, Zikai Wei, Siyu Luo,et al. Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows[J]. Transportation Research Part E,2024,192, 103798. DOI: https://doi.org/10.1016/j.tre.2024.103798

[3] Bai Q, Yin X, Lim M K, et al. Low-carbon VRP for cold chain logistics considering real-time trafficconditions in the road network [J]. Industrial management & data systems, 2022, 122(2): 521-543. DOI: https://doi.org/10.1108/IMDS-06-2020-0345

[4] Qiu Jinhong, Sun Jing, Zhong Zhaoman. A multi-objective green vehicle routing optimization algorithm based on delivery benefit balance [J]. Control and Decision , 2021, 12(3): 1-7.

[5] Ganji M, Kazemipoor H, Molana S. A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows[J]. Journal of Cleaner Production, 2020, 259: 120824. DOI: https://doi.org/10.1016/j.jclepro.2020.120824

[6] He Qi, Guan Lihe, Cui Huanhuan. Hybrid Variable Neighborhood Tabu Search Algorithm for Vehicle Routing Problem with Hard Time Window[J].Computer Engineering and Applications,2023,59(13):82-91.

[7] Zhang, Zixian, Geqi Qi, and Wei Guan. Coordinated multi‐agent hierarchical deep reinforcement learning to solve multi‐trip vehicle routing problems with soft time windows[J]. IET Intelligent Transport Systems, 2023,17(10): 2034-2051. DOI: https://doi.org/10.1049/itr2.12394

[8] Chen Xiqiong,Hu Dawei, Yang Qianqian, et al. An improved ant colony algorithm for multi-objective vehicle routingproblem with simultaneous pickup and delivery[J]. Control Theory&Applications, 2018, 35(9): 1347-1356.

[9] Shang Zhengyang, Gu Jinan, Pan Jiabao. 2L-CVRP vehicle routing problem with LIFO loading constraint[J]. Computer Integrated Manufacturing Systems, 2021, 27(7): 2134-2143.

[10] Wang Yong, Zuo Jiaxing, Jiang Qiong, et al. Vehicle Routing Optimization of Reverse LogisticsBased on Product Recovery Pricing[J].Journal of Systems &Management, 2022, 31(2): 199-216.

[11] XU Jun-Xiang,ZHANG Jin,GUO Jing-ni.Research on Unmanned Vehicle Routing Problem with Variable Travel Time [J]. Industrial Engineering and Management, 2019, 24 (5): 120-131.

[12] Wang Lei,Wang Xin,Liu Dehai,Hu Hui.Route Optimization Methodology for Unmanned Vehicle Distribution in Intelligent Network[J].2020,40(11):1984-1998.

[13] Wang Yuqin,Hu Hui,Liu Fuxin,et al. Optimization of Unmanned Vehicle Distribution Path under Intelligent Network Connection. Operations Research and Management Science [J]. 2021,30(8):52-58.

[14] Zhao, Jiale, et al. Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm. Computer Communications[J]. 2024, 216: 112-129. DOI: https://doi.org/10.1016/j.comcom.2023.12.040

[15] Nayak, Abhishek, and Sivakumar Rathinam. Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem[J]. Sensors,2023, 23(14): 6432. DOI: https://doi.org/10.3390/s23146432

[16] Peng, Shuyue, Qinming Liu, and Jiarui Hu. Green Distribution Route Optimization of Medical Relief Supplies Based on Improved NSGA-II Algorithm under Dual-Uncertainty[J]. Sustainability, 2023,15(15): 11939. DOI: https://doi.org/10.3390/su151511939

Downloads

Published

28-11-2024

Issue

Section

Articles

How to Cite

Liu, Z., Liu, D., & Liu, S. (2024). Optimization of Unmanned Vehicle Delivery Routes Considering Charging and Time Windows. Frontiers in Computing and Intelligent Systems, 10(2), 79-87. https://doi.org/10.54097/tg21v812