Abstract
Logistics distribution path planning is at the core of the logistics industry transportation process, but because logistics distribution accounts for a large proportion of the total logistics cost, so the optimization of distribution vehicle path is a hotspot of current research. This paper mainly studies logistics distribution path planning and design based on ant colony optimization algorithm. In this paper, the traditional ant colony algorithm (ACA) is optimized and improved by introducing constraints such as vehicle distance and load capacity, taking cost and load capacity as optimization objectives, and the physical distribution path is planned and designed by using the optimized ACA. The logistics data of a certain logistics enterprise in Shanghai is simulated and compared with genetic algorithm and ACA. The experimental results show that the proposed ant colony optimization algorithm saves 5.4% of the total transportation cost compared with the ACA and 2.7% of the total transportation cost compared with the genetic algorithm.
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References
Breunig, U., Baldacci, R., Hartl, R.F., et al.: The electric two-echelon vehicle routing problem. Comput. Oper. Res. 103, 198–210 (2019)
Bernal, J., Escobar, J.W., Linfati, R.: A simulated annealing-based approach for a real case study of vehicle routing problem with a heterogeneous fleet and time windows. Int. J. Shipping Transp. Logist. 13(1/2), 185 (2021)
Kleff, A., Bräuer, C., Schulz, F., Buchhold, V., Baum, M., Wagner, D.: Time-dependent route planning for truck drivers. In: Bektaş, T., Coniglio, S., Martinez-Sykora, A., Voß, S. (eds.) Computational Logistics. ICCL 2017. LNCS, vol. 10572, pp. 110–126. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68496-3_8
Mainwaring, G., Olsen, T.O.: Long undersea tunnels: recognizing and overcoming the logistics of operation and construction. Engineering 4(2), 249–253 (2018)
Loske, D., Klumpp, M.: Human-AI collaboration in route planning: an empirical efficiency-based analysis in retail logistics. Int. J. Prod. Econ. 241, 108236 (2021)
Verma, A., Campbell, A.M.: Strategic placement of telemetry units considering customer usage correlation. EURO J. Transp. Logist. 8(1), 35–64 (2017). https://doi.org/10.1007/s13676-017-0104-9
Husain, N.P., Arisa, N.N., Rahayu, P.N., et al.: Least squares support vector machines parameter optimization based on improved aca for hepatitis diagnosis. Jurnal Ilmu Komputer dan Informasi 10(1), 43 (2017)
Rahimi, M., Kumar, P., Yari, G.: Portfolio selection using aca and entropy optimization. Pak. J. Stat. 33(6), 441–448 (2017)
Sekiner, S.U., Shumye, A., Geer, S.: Minimizing solid waste collection routes using ACA: a case study in gaziantep district. J. Transp. Logist. 6(1), 29–47 (2021)
Saramud, M.V., Kovalev, I.V., Kovalev, D.I., et al.: Modification of the ACA for multiversion software application development. In: IOP Conference Series: Materials Science and Engineering, vol. 1047, no. 1, p. 012155 (9pp) (2021)
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Wang, Y. (2023). Logistics Distribution Path Planning and Design Based on Ant Colony Optimization Algorithm. In: Xu, Z., Alrabaee, S., Loyola-González, O., Cahyani, N.D.W., Ab Rahman, N.H. (eds) Cyber Security Intelligence and Analytics. CSIA 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-031-31775-0_6
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DOI: https://doi.org/10.1007/978-3-031-31775-0_6
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