Hybrid Random Forest Regression and Ant Colony Optimization for Delivery Route Optimization
Abstract
The transportation of goods in Indonesian cities is increasingly challenged by urbanization, congestion, diverse road characteristics, and environmental factors, reducing the effectiveness of conventional distance-based routing. This study enhances delivery route optimization by integrating travel-time prediction using Random Forest Regression (RFR) with a metaheuristic routing process using Ant Colony Optimization (ACO). Using OpenStreetMap (OSM) data for Palembang, experiments were conducted on five simulated customer locations in Zone 1. Road attributes such as segment length, road type, and estimated speed were used to train the RFR model, whose predicted travel times served as dynamic costs in the ACO heuristic. The RFR model achieved high predictive accuracy (R² = 0.98; MSE = 8.81), and the ACO-based optimization produced an efficient route of 29.58 km with a total travel time of 148 minutes. However, the experiment is limited to a single zone, a small number of customers, and the removal of real traffic variables—where all actual speed variations, congestion levels, and time-dependent traffic conditions were simplified or omitted, causing the model to rely solely on static road attributes. Future work will incorporate real-time traffic data, expand testing to multiple zones, and use larger datasets to improve scalability and operational applicability.
Downloads
References
A. Nasik, K. Karyanto, and R. C. Wibowo, “Identifikasi bencana banjir di kota palembang berdasarkan data klimatologi tahun 2021 dengan pendekatan curve fitting dan model inversi garis,” J. Teknol. dan Inov. Ind., vol. 6, no. 1, pp. 38–45, 2025, doi: 10.23960/jtii.v6i1.105.
M. Santo Gitakarma, “Analisis Kelas Kompleksitas Berdasarkan Berbagai Jenis Mesin Turing Dalam Teori Komputasi,” J. Komput. dan Teknol. Sains, vol. 3, no. 1, pp. 10–17, 2024.
M. F. Ardiansyah, “Implementasi Algoritma Semut Dalam Penentuan Rute Terpendek Pendistribusian Barang Pt Wings Surya Di Wilayah Surabaya Timur,” 2024.
W. Liu, “Route Optimization for Last-Mile Distribution of Rural E-Commerce Logistics Based on Ant Colony Optimization,” IEEE Access, vol. 8, pp. 12179–12187, 2020, doi: 10.1109/ACCESS.2020.2964328.
P. Stodola, K. Michenka, J. Nohel, and M. Rybanský, “Hybrid algorithm based on ant colony optimization and simulated annealing applied to the dynamic traveling salesman problem,” Entropy, vol. 22, no. 8, 2020, doi: 10.3390/E22080884.
S. He, “A novel travel route planning method based on an ant colony optimization algorithm,” Open Geosci., vol. 15, no. 1, 2023, doi: 10.1515/geo-2022-0541.
S. Liang, T. Jiao, W. Du, and S. Qu, “An improved ant colony optimization algorithm based on context for tourism route planning,” PLoS One, vol. 16, no. 9 September, pp. 1–16, 2021, doi: 10.1371/journal.pone.0257317.
J. Cai, K. Xu, Y. Zhu, F. Hu, and L. Li, “Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest,” Appl. Energy, vol. 262, no. January, p. 114566, 2020, doi: 10.1016/j.apenergy.2020.114566.
J. Peng, K. Manevski, K. Kørup, R. Larsen, and M. N. Andersen, “Random forest regression results in accurate assessment of potato nitrogen status based on multispectral data from different platforms and the critical concentration approach,” Field Crops Research, vol. 268. 2021, doi: 10.1016/j.fcr.2021.108158.
M. Randriamihaja et al., “Combining OpenStreetMap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile,” PLOS Digit. Heal., vol. 3, no. 11, pp. 1–21, 2024, doi: 10.1371/journal.pdig.0000621.
T. Wang, Q. Zhao, W. Gao, and X. He, “Research on energy consumption in household sector: a comprehensive review based on bibliometric analysis,” Front. Energy Res., vol. 11, no. January, pp. 1–22, 2023, doi: 10.3389/fenrg.2023.1209290.
Z. A. Çil, S. Mete, and F. Serin, “Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach,” Appl. Math. Model., vol. 86, pp. 335–348, 2020, doi: 10.1016/j.apm.2020.05.006.
S. Li, Y. Wei, X. Liu, H. Zhu, and Z. Yu, “A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm,” Mathematics, vol. 10, no. 6, 2022, doi: 10.3390/math10060925.
Y. Tao et al., “A mobile service robot global path planning method based on ant colony optimization and fuzzy control,” Appl. Sci., vol. 11, no. 8, 2021, doi: 10.3390/app11083605.
D. Sun, J. Xu, H. Wen, and D. Wang, “Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest,” Eng. Geol., vol. 281, p. 105972, 2021, doi: 10.1016/j.enggeo.2020.105972.
R. Hariharan, “Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: A case study of New Delhi,” Urban Clim., vol. 36, no. October 2020, p. 100780, 2021, doi: 10.1016/j.uclim.2021.100780.
P. Nie, M. Roccotelli, M. P. Fanti, Z. Ming, and Z. Li, “Prediction of home energy consumption based on gradient boosting regression tree,” Energy Reports, vol. 7, pp. 1246–1255, 2021, doi: 10.1016/j.egyr.2021.02.006.
E. Izquierdo-Verdiguier and R. Zurita-Milla, “An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing,” Int. J. Appl. Earth Obs. Geoinf., vol. 88, no. October 2019, p. 102051, 2020, doi: 10.1016/j.jag.2020.102051.
K. J. Miraswan and M. Utari, “Perbandingan Prediksi Curah Hujan Kota Palembang Menggunakan Metode Fuzzy Time Series Chen dan Lee,” JSI J. Sist. Inf., vol. 17, no. 1, pp. 214–227, 2025, doi: 10.18495/jsi.v17i1.245.
C. Arransz, “Determining the Number of Ants in Ant Colony Optimization,” J. Biomed. Sustain. Healthc. Appl., vol. 3, no. 1, pp. 76–86, 2023, doi: 10.53759/0088/jbsha202303008.
T. Nurcahyadi and C. Blum, “Adding negative learning to ant colony optimization: A comprehensive study,” Mathematics, vol. 9, no. 4, pp. 1–23, 2021, doi: 10.3390/math9040361.
S. S. Singh, K. Singh, A. Kumar, and B. Biswas, “ACO-IM: maximizing influence in social networks using ant colony optimization,” Soft Comput., vol. 24, no. 13, pp. 10181–10203, 2020, doi: 10.1007/s00500-019-04533-y.
W. Li, L. Xia, Y. Huang, and S. Mahmoodi, “An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 3, pp. 1557–1571, 2022, doi: 10.1007/s12652-021-03120-0.
Q. Song, Q. Zhao, S. Wang, Q. Liu, and X. Chen, “Dynamic path planning for unmanned vehicles based on fuzzy logic and improved ant colony optimization,” IEEE Access, vol. 8, pp. 62107–62115, 2020, doi: 10.1109/ACCESS.2020.2984695.
F. Zhang et al., "Route Searching Based On Neural Networks And Heuristic Reinforcement Learning," PLoS ONE, vol. 12, no. 2, p. e0172054, Feb. 2017.
Abstract views: 60 times
Download PDF: 36 times
Copyright (c) 2025 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
- I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
- I certify that all authors have approved the publication of this and there is no conflict of interest.
- I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
- I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
- I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
- If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
- I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)
