Logistics distribution route optimization of electric vehicles based on distributed intelligent system
Abstract
The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution.
-
Research ethics: Not applicable.
-
Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: The author states no conflict of interest
-
Research funding: This work was surported by Research and Practice on the Training Path of Management Talents in Supply Chain Enterprises in Liaoning Province [JYTMS20231864]; General project of basic scientific research project of Liaoning Provincial Department of Education in 2023 (unveiling the list and serving local projects).
-
Data availability: Not applicable.
References
1. Liu, Z, Jia, L. Research on routing optimisation of logistics distribution vehicle based on cloud model. Int J Inf Technol Manag 2021;20:265–81. https://doi.org/10.1504/ijitm.2021.10037422.Search in Google Scholar
2. Zulkarnain, Z, Komarudin, K, Arofah, F, Rahman, I. Developing an intelligent logistics and distribution system for a large number of retail outlets: a big data analytics approach. CSID J Infrastruct Dev 2020;3:28–39. https://doi.org/10.32783/csid-jid.v3i1.106.Search in Google Scholar
3. Yong, H, Zhou, H, Wang, Y. Research and application of genetic algorithm in path planning of logistics distribution vehicle. AIP Conf Proc 2017;1864:20047–8. https://doi.org/10.1063/1.4992864.Search in Google Scholar
4. Mu, DA, Nan, KB, Xh, C, Zhao, L. Tactical production and distribution planning in urban logistics under vehicle operational restrictions-ScienceDirect. Procedia Comput Sci 2018;126:1720–9. https://doi.org/10.1016/j.procs.2018.08.105.Search in Google Scholar
5. Deng, Y, Yu, Q, Li, B, Gong, R, Chang, G. Power supply mode planning of electric vehicle participating in logistics distribution based on battery charging and swapping station. Int J Emerg Elec Power Syst 2019;20:45–9. https://doi.org/10.1515/ijeeps-2018-0232.Search in Google Scholar
6. Jiang, J, Yu, N, Ye, J, Bai, W. Vehicle logistics path optimization based on ant colony and particle hybrid algorithm. J Phys Conf 2021;1865:042086–7. https://doi.org/10.1088/1742-6596/1865/4/042086.Search in Google Scholar
7. Lai, M, Yang, H, Yang, S, Zhao, J, Xu, Y. Cyber-physical logistics system-based vehicle routing optimization. J Ind Manag Optim 2017;10:701–15. https://doi.org/10.3934/jimo.2014.10.701.Search in Google Scholar
8. Gao, X, Liu, L. Green intelligent logistics sorting system in big data environment. IOP Conf Ser Mater Sci Eng 2020;711:012040–6. https://doi.org/10.1088/1757-899x/711/1/012040.Search in Google Scholar
9. Yao, LJ, Li, J, Han, JJ. Application of virtual reality technology in intelligent cold chain logistics system. J Phys Conf 2020;1651:012030–5. https://doi.org/10.1088/1742-6596/1651/1/012030.Search in Google Scholar
10. Lin, H. Optimization model of shortest distribution path selection in intelligent logistics. J Phys Conf 2020;1574:012171–9. https://doi.org/10.1088/1742-6596/1574/1/012171.Search in Google Scholar
11. Li-Zhu, WU, University, SV. Design of logistics distribution intelligent system based on RFID and two-dimensional code. Digit Technol Appl 2019;685:1847–52.Search in Google Scholar
12. Yu, J. Two-stage request scheduling for autonomous vehicle logistic system. IEEE Trans Intell Transport Syst 2018;99:1–13.Search in Google Scholar
13. Lin, CC, Yang, JW. Cost-efficient deployment of fog computing systems at logistics centers in industry 4.0. IEEE Trans Ind Inf 2018;74:885–91.10.1109/TII.2018.2827920Search in Google Scholar
14. Kumar, RS, Rani, C, Kumar, PG. Design of smart logistics transportation system using MapReduce intelligent water drops algorithm in Hadoop environment. Int J Logist Syst Manag, 2018, 31:249-64, https://doi.org/10.1504/ijlsm.2018.094937.Search in Google Scholar
15. Phu, DN, Tuan, VD, Duong, TB, Uyen, ND, Tran, DT. Numerical method of synthesizing the automatic electric drive system containing elastically linked elements. In: Intelligent systems and networks: selected articles from ICISN 2022. Vietnam, Singapore: Springer Nature Singapore; 2022:53–61 pp.10.1007/978-981-19-3394-3_7Search in Google Scholar
16. Paul, R, Tait, A. The international review of research in open and distributed. Learning 2019;20. https://doi.org/10.19173/irrodl.v20i4.4575.Search in Google Scholar
17. Agarwal, S, Makkar, S, Tran, D-T, editors. Privacy vulnerabilities and data security challenges in the IoT. Boca Raton: CRC Press; 2020.10.1201/9780429322969Search in Google Scholar
18. Gunjan, VK, Singh, SN, Duc-Tan, T. Rincon Aponte GJ, Kumar. System reliability, quality control, safety, maintenance and management. Singapore: Springer (Scopus Indexed); 2019.Search in Google Scholar
19. Chen, Y, Zheng, W, Li, W, Huang, Y. The robustness and sustainability of port logistics systems for emergency supplies from overseas. J Adv Transport 2020;2020:8868533. https://doi.org/10.1155/2020/8868533.Search in Google Scholar
20. Zhang, X, Wang, Y, Yuan, X, Shen, X, Lu, Z. Adaptive dynamic surface control with disturbance observers for battery/supercapacitor-based hybrid energy sources in electric vehicles. IEEE Trans Transport Electrif 2022;9:5165–81. https://doi.org/10.1109/TTE.2022.3194034.Search in Google Scholar
21. Ning, T, Wang, J, Han, Y. Logistics distribution de-carbonization pathways and effect in China: a systematic analysis using VRPSDP model. Int J Low Carbon Technol 2021;16:1404–11. https://doi.org/10.1093/ijlct/ctab063.Search in Google Scholar
22. Kim, D. Design of an all-electric bus cooling system incorporating computational fluid dynamics. Kinet Mech Eng 2022;3:28–37. https://doi.org/10.38007/KME.2022.030104.Search in Google Scholar
23. Zhou, Z, Liu, Z, Su, H, Zhang, L. Intelligent path planning strategy for electric vehicles combined with urban electrified transportation network and power grid. IEEE Syst J 2022;16:2437–47. https://doi.org/10.1109/jsyst.2021.3075088.Search in Google Scholar
24. Abdulkader, M, Gajpal, Y, Elmekkawy, TY. Vehicle routing problem in omni-channel retailing distribution systems. Int J Prod Econ 2018;196:43–55. https://doi.org/10.1016/j.ijpe.2017.11.011.Search in Google Scholar
25. Charu, E. Improvement and implementation of electric vehicle charging optimization strategy based on multi-body dynamics. Kinet Mech Eng 2022;3:38–46. https://doi.org/10.38007/KME.2022.030105.Search in Google Scholar
26. Chen, T, Gao, C. Intelligent electric vehicle charging scheduling in transportation-energy nexus with distributional reinforcement learning. IEEE CAA J Autom Sinica 2023;10:2171–3. https://doi.org/10.1109/jas.2023.123285.Search in Google Scholar
27. Xiao, JH, Chen, P, Liu, BL. Research on parking pricing of logistics distribution vehicle under urban traffic congestion. J Bus Econ 2018;38:16–23.Search in Google Scholar
28. Yael, P. Data collection function of ferroelectric memory in intelligent high-speed vehicle monitoring system. Distributed Process Syst 2021;2:42–57. https://doi.org/10.38007/DPS.2021.020104.Search in Google Scholar
29. Du, J, Li, X, Sugumaran, V, Hu, Y, Yan, X. Dynamic model averaging-based procurement optimization of prefabricated components. Neural Comput Appl 2023;35:25157–73. https://doi.org/10.1007/s00521-023-08715-6.Search in Google Scholar
30. Selvarajan, S, Manoharan, H, Khadidos, AO, Shankar, A, Khadidos, AO, Onyema, EM. Obstacles uncovering system for slender pathways using unmanned aerial vehicles with automatic image localization technique. Int J Comput Intell Syst 2023;16:164. https://doi.org/10.1007/s44196-023-00344-0.Search in Google Scholar
31. Shankar, A. Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system. Artif Intell Rev 2023;56:15053–77. https://doi.org/10.1007/s10462-023-10519-y.Search in Google Scholar
32. Yang, D, Wu, Y. Intelligent algorithm of vehicle routing optimization for logistics distribution. Bol Tec/Tech Bull 2017;55:512–20.Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Special Issue: Energy Storage and Management System for Electric Vehicles; Guest Editors: Achyut Shankar and Zahid Akhtar
- Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm
- Investigation on voltage stability evaluation indicators and algorithms for power systems based on neural network algorithms
- Collaborative optimization algorithm for electric vehicle industry chain based on regional economic development needs
- Distributed generation aggregators considering low-carbon credits optimize dispatch strategies
- CFD simulation analysis optimization and experimental verification of heat dissipation problem of electric vehicle motor controller
- Logistics distribution route optimization of electric vehicles based on distributed intelligent system
- Analysis of green energy regeneration system for Electric Vehicles and Re estimation of carbon emissions in international trade based on evolutionary algorithms
- Regular Articles
- Location of faults based on deep learning with feature selection for meter placement in distribution power grids
- A novel computational technique to analyse the corona generated ionized field environment of EHV/UHV DC transmission lines
- Improving protection of compensated transmission line using IoT enabled adaptive auto reclosing scheme
- A new optimized ZV-ZCS non-isolated high-gain boost converter for renewable energy systems
- Performance of PMU in an electric distribution grid during transients
Articles in the same Issue
- Frontmatter
- Special Issue: Energy Storage and Management System for Electric Vehicles; Guest Editors: Achyut Shankar and Zahid Akhtar
- Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm
- Investigation on voltage stability evaluation indicators and algorithms for power systems based on neural network algorithms
- Collaborative optimization algorithm for electric vehicle industry chain based on regional economic development needs
- Distributed generation aggregators considering low-carbon credits optimize dispatch strategies
- CFD simulation analysis optimization and experimental verification of heat dissipation problem of electric vehicle motor controller
- Logistics distribution route optimization of electric vehicles based on distributed intelligent system
- Analysis of green energy regeneration system for Electric Vehicles and Re estimation of carbon emissions in international trade based on evolutionary algorithms
- Regular Articles
- Location of faults based on deep learning with feature selection for meter placement in distribution power grids
- A novel computational technique to analyse the corona generated ionized field environment of EHV/UHV DC transmission lines
- Improving protection of compensated transmission line using IoT enabled adaptive auto reclosing scheme
- A new optimized ZV-ZCS non-isolated high-gain boost converter for renewable energy systems
- Performance of PMU in an electric distribution grid during transients
