Solution of the Transportation Problem Employing Intelligent Technologies
DOI:
https://doi.org/10.18372/1990-5548.78.18281Keywords:
logistics, transport logistics, model, simulation model, simulation, optimization, artificial intelligence, neural networkAbstract
The article presents a new method of predicting the time of completion of a transport task. The method is based on the created mathematical model of the transport task process and on artificial intelligence methods. The choice of the optimal transportation route is based on the conditions of a specific task. A new method of choosing the optimal route is presented, taking into account the influence of such factors as weather conditions, day of the week, time of day, condition of the road surface, presence of residential complexes and other factors. The purpose of the study is to optimize the method of building a transport route using neural networks. The work presents the improvement of the general method of optimizing the transport process, the development of a neural network training method for predicting the time of the transport process, the development of a simulation mathematical model of the transport process and the determination of the model parameters and the effectiveness of the method verification. Research methods: methods of mathematical modeling, simulation modeling, Monte Carlo method, methods of artificial intelligence based on neural networks.
References
N. Slimani, N. Sbiti, and M. Amghar, “Traffic forecasting in Morocco using artificial neural networks,” Procedia Computer Science, vol. 151, 2019, pp. 471–476. https://doi.org/10.1016/j.procs.2019.04.064.
S. Nuli1, N. Vikranth, and K. Gupta, “Published under licence by IOP Publishing Ltd,” IOP Conference Series: Earth and Environmental Science. 2022 Advancements in Sustainable Materials and Infrastructure 24/08/2022–25/08/2022 Hyderabad, India: 1086. https://doi.org/10.1088/1755-1315/
T. Wongpiromsarn, T. Uthaicharoenpong, Y. Wang, E. Frazzoli and D. Wang, "Distributed traffic signal control for maximum network throughput," 2012 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, USA, 2012, pp. 588-595, doi: 10.1109/ITSC.2012.6338817.
Zhihan Lv and Wenlong Shang, “Impacts of Intelligent Transportation Systems on Energy Conservation and Emission Reduction of Transport Systems: A comprehensive review,” Green Technologies and Sustainability, vol. 1, Issue 1, January 2023, 100002. https://doi.org/10.1016/j.grets.2022.100002.
O. S. Yakushenko, M. R. Trakhanovska, and O. O. Satayeva, “The use of a simulation model to evaluate the performance of a time transport task,” MNPC "Problems of organization of aviation, multimodal transportation and the application of aviation in economic sectors", 27.10.2020.
Matlab https://ru.wikipedia.org/wiki/MATLAB last accessed 2024/01/08.
O. S. Yakushenko and M. R. Trakhanovska “The use of neural networks in the optimization of transportation routes,” MNPC "Problems of organization of aviation, multimodal transportation and the application of aviation in economic sectors", 27.10.2020.
V. S. Medvedev and V. G. Potemkin, Neural networks. MATLAB 6. Under general ed. Moscow, DIALOG-MYFI, 2002, 496 p.
I.M. Udovik, G.M. Korotenko, L.M. Korotenko, V.A. Trusov, A.T. Kharj. The manual: ARTIFICIAL INTELLIGENCE METHODS AND SYSTEMS for 122 specialty «Computer Science» / life .:– D.: State University «National Mining University», 2017. – 100 p.
H. Yu and B. M. Wilamowski, Levenberg–Marquardt Training, https://www.eng.auburn.edu/~wilambm/pap/2011/K10149_C012.pdf last accessed 2024/01/09
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
