Application of Spatial Temporal Graph Neural Network in Analyzing the Distribution of Goods Shipping with Dominating Set Technique

Year 2024, Volume: 11 Issue: 1, 10 - 22, 13.03.2024

Ika Hesti Agustin Binti Arianti Dafik Dafık Mohamad Fatekurohman Rifki Ilham Baihaki

https://doi.org/10.31202/ecjse.1289020

Abstract

A logistics service company may face capacity issues due to distribution delays, resulting in goods accumulating in branch offices with unknown locations. To resolve this problem, we will implement the Spatial-Temporal Graph Neural Network (STGNN) combined with the dominating set technique to predict these branch office locations. The STGNN utilizes graph theory to represent relationships between branch offices in Indonesia. Simulation data on goods shipments across Indonesia are observed for 30 days, categorized as spatial-temporal data, and fed into the STGNN. This process involves three stages: node embeddings, training, and testing/forecasting. We implement some Artificial Neural Network (ANN) models with various hidden layer architectures. The results show that the best model of ANN is cascade forward metwork and the MSE 1,6714×〖10〗^(-9).

Keywords

Spatial-Temporal Graph Neural Network, Goods Shipping, Logistics Services, Dominating Set

References

• [1]URL. (Accessed December 2, 2022). [Online]. Available: https://kargo.tech/blog/daftar-jasa-kurir-pengiriman-barang-yang-mana-pilihanmu/
• [2]——. (Accessed November 7, 2022). [Online]. Available: https://kargo.tech/blog/5-jasa-ekspedisi-yang-melayani-pengiriman-barang-besar-2/
• [3]Eko. (2022) Jne trucking, more than 20 days and the package has not been received yet. (Accessed November 7, 2022). [Online]. Available: https://www.google.com/amp/s/mediakonsumen.com/2022/10/17/surat-pembaca/jne-trucking-lebih-dari-20-hari-paket-masih-belum-diterima/amp
• [4]R. Kumalasari. Everything you need to know about shipping goods!
• [5]D. Harliyuni, Dafik, Slamin, Z. R. Ridlo, and R. Alfarisi, ‘‘On the spatial graph neural network analysis together with local vertex irregular reflexive coloring for time series forecasting on passenger density at bus station,’’ Advances in Intelligent System Research, vol. 177, pp. 305–323, 2023.
• [6]S. A. H. Alrubaie, ‘‘Cascade-forward neural network for volterra integral equation solution,’’ Ibn Al Haitham Journal for Pure and Applied Science, vol. 34, pp. 104–114, 2021.
• [7]L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms and Applications. Pearson, 1993. [8]S. Setti, I. A. R. Simbolon, M. Syafiq, and I. Parlina, ‘‘The application of artificial neural network in predicting the amount of crude oil exports in indonesia,’’ Journal Of Informatics And Telecommunication Engineering, vol. 2, pp. 31–38, 2018.
• [9]A. Gedik, ‘‘Short-term traffic volume prediction for the merging roads by artificial neural network,’’ El-Cezeri Journal of Science and Engineering, vol. 7, no. 3, pp. 1496–1508, 2020.
• [10]P. Yilmaz, S. Akcakaya, S. Ozkaya, and A. Cetin, ‘‘Machine learning based music genre classification and recommendation system,’’ El-Cezeri Journal of Science and Engineering, vol. 9, no. 4, pp. 1560–1571, 2022. [11]M. R. T. Dale, Applying Graph Theory in Ecological Research. TJ International Ltd, 2017.
• [12]Kiki and S. Kusumadewi, ‘‘Artificial neural network with backpropagation method for detecting psychological disorders,’’ Media Informatika, vol. 2, pp. 1–11,2004.
• [13]W. L. Hamilton, Graph Representation Learning. Morgan & Claypool Publisher, 2020.
• [14]A. Muklisin, I. M. Tirta, Dafik, R. I. Baihaki, and A. I. Kristiana, ‘‘The analysis of airport flow by using spatial-temporal graph neural networks and resolving efficient dominating set,’’ Advances in Intelligent System Research, vol. 177, pp. 3–20, 2023.
• [15]A. Singh, ‘‘Classification of malware in https traffic using machine learning approach,’’ El-Cezeri Journal of Science and Engineering, vol. 9, no. 2, pp. 644–655, 2022.
• [16]G. Chartrand and P. Zhang, A First Course in Graph Theory. Dover Publications, 2012.
• [17]J. G. Nicholls, A. R. Martin, P. A. Fuchs, D. A. Brown, M. E. Diamond, and D. A. Weisblat, From Neuron to Brain, Fifth Edition. Sinauer Associates, Inc., 2012.
• [18]M. Aziza, Dafik, A. Kristiana, R. Alfarisi, and D. Wardani, ‘‘On resolving perfect dominating number of comb product of special graphs,’’ Journal of Physics: Conference Series, vol. 1832, 2021.
• [19]Dafik, I. H. Agustin, and A. R. Wardani, ‘‘The number of locating independent dominating set on generalized corona product graphs,’’ Advances in Mathematics Scientific Journal, vol. 9, pp. 4873–4891, 2020.
• [20]D. A. R. Wardani, Dafik, and I. H. Agustin, ‘‘The locating dominating set (lds) of generalized of corona product of path graph and any graphs,’’ Journal of Physics: Conference Series, vol. 1465, 2020.
• [21]N. Mauliska, W. Lestari, E. T. Wisudaningsih, and M. H. Islam, ‘‘Application of spatial-temporal graph neural networks for forecasting data time series river pollution waste content in probolinggo,’’ Advances in Intelligent System Research, vol. 177, pp. 257–272, 2023.