(Universitas Widyatama, Bandung, Jawa Barat, 40125, Indonesia)
(2) Verani Hartati
(Universitas Widyatama, Bandung, Jawa Barat, 40125, Indonesia)*Corresponding author
AbstractThis study addresses inefficiencies in route planning for the distribution of perishable goods by a small-scale enterprise, Jaya Abadi Fruits, located in East Jakarta. The current manual delivery planning, reliant on driver experience, results in suboptimal distances, increased fuel usage, and inconsistent service quality. To overcome these challenges, the Ant Colony Optimization (ACO) algorithm was applied to a Capacitated Vehicle Routing Problem (CVRP) and implemented using MATLAB. The model incorporates real-world parameters such as delivery distances, box dimensions, demand volume, and vehicle capacities. Simulation results demonstrate significant improvements: for Vehicle 1, travel distance and distribution cost were reduced by 41.93% and 15.6%, respectively; for Vehicle 2, distance decreased by 30.96% and cost by 2.03%. These findings validate ACO as an effective, low-cost, and scalable decision-support tool for logistics operations in small enterprises lacking integrated digital infrastructure. The research contributes to the optimization of last-mile delivery in resource-limited supply chains, particularly in emerging economies.
KeywordsCapacitated Vehicle Routing Problem (CVRP); Ant Colony Optimization (ACO); Perishable Distribution; MATLAB; Small Enterprise Logistics; Route Optimization
|
DOIhttps://doi.org/10.33122/ejeset.v6i1.729 |
Article metricsAbstract views : 575 | PDF views : 357 |
Cite |
Full Text Download
|
References
Chai, Y. Y., & Johar, F. (2024). Optimizing Blood Transport Costs: Ant Colony Optimization Method for Vehicle Routing Problem with Time Windows. Proceedings of Science and Mathematics, Volume 21, 11–19.
Chen, Y., Wei, J., Luo, T., & Zhou, J. (2025). Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems. Complex and Intelligent Systems, 11(5), 1–21. https://doi.org/10.1007/s40747-025-01826-8
Choudhari, A., Ekbote, A., & Chaudhuri, P. (2022). Capacitated Vehicle Routing Problem Using Conventional and Approximation Method. https://doi.org/10.48550/arXiv.2208.00046
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39. https://doi.org/10.1109/MCI.2006.329691
Feng, Q., Zhao, G., Li, W., & Shi, X. (2023). Distribution Path Optimization of Fresh Products in Cold Storage Considering Green Costs. In Buildings (Vol. 13, Issue 9). https://doi.org/10.3390/buildings13092325
Gaida, I. W. E., & Mittal, M. (2022). Efficient Supply chain delivery planning considering dynamic route selection using Ant Colony Optimization. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1–8. https://doi.org/10.1109/ICRITO56286.2022.9964847
He, M., Yang, M., Fu, W., Wu, X., & Izui, K. (2024). Optimization of Electric Vehicle Routes Considering Multi-Temperature Co-Distribution in Cold Chain Logistics with Soft Time Windows. In World Electric Vehicle Journal (Vol. 15, Issue 3). https://doi.org/10.3390/wevj15030080
Ivkovic, N., Kudelić, R., & Golub, M. (2023). Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information. Algorithms, 16, 251. https://doi.org/10.3390/a16050251
Juhászné Bíró, T., & Németh, P. (2023). The Importance and Applicability of Metaheuristics in Supply Chains: Trends, Gaps, and Methodologies. In A. Taghipour (Ed.), Blockchain Applications in Cryptocurrency for Technological Evolution (pp. 249–267). IGI Global. https://doi.org/10.4018/978-1-6684-6247-8.ch015
Laporte, G. (2009). Fifty Years of Vehicle Routing. Transportation Science, 43, 408–416. https://doi.org/10.1287/trsc.1090.0301
Okwu, M., & Tartibu, L. (2020). Ant Colony Algorithm (pp. 33–41). https://doi.org/10.1007/978-3-030-61111-8_4
Puspitasari, F. H., & Kurniawan, V. R. B. (2021). Designing Optimal Distribution Routes using a Vehicle Routing Problem (VRP) Model in a Logistics Service Provider. IOP Conference Series: Materials Science and Engineering, 1071(1), 012005. https://doi.org/10.1088/1757-899x/1071/1/012005
Qi, B., & Li, G. (2024). The evolution of the cold chain logistics vehicle routing problem : a bibliometric and visualization revie.
Seyyedabbasi, A., & Kiani, F. (2020). MAP-ACO: An efficient protocol for multi-agent pathfinding in real-time WSN and decentralized IoT systems. Microprocessors and Microsystems, 79, 103325. https://doi.org/https://doi.org/10.1016/j.micpro.2020.103325
Shi, H., Zhang, Q., & Qin, J. (2024). Cold Chain Logistics and Joint Distribution: A Review of Fresh Logistics Modes. In Systems (Vol. 12, Issue 7). https://doi.org/10.3390/systems12070264
Suryawanshi, P., & Dutta, P. (2023). Distribution planning problem of a supply chain of perishable products under disruptions and demand stochasticity. International Journal of Productivity and Performance Management, 72(1), 246–278. https://doi.org/10.1108/IJPPM-12-2020-0674
Syaifudin, A., & Handayani, W. (2024). Pengembangan Model Perutean Kendaraan Berbasis Green Logisticdalam Pendistribusian Makanan Ringan Pada Ud Sumber Rejeki. Modeling and Optimization in Green Logistics, 8(6), 1–9.
Syamil, A., Nusantara, B., Waty, E., & Fahmi, M. A. (2023). Buku Ajar Manajemen Rantai Pasok. https://www.researchgate.net/publication/373980212
Tandon, R., & Gupta, P. (2022). ACHM: An Efficient Scheme for Vehicle Routing Using ACO and Hidden Markov Model (pp. 169–180). https://doi.org/10.1007/978-3-031-21385-4_15
Valdez, F., Moreno, F., & Melin, P. (2020). A Comparison of ACO, GA and SA for Solving the TSP Problem (pp. 181–189). https://doi.org/10.1007/978-3-030-34135-0_13
Wang, Y., Chen, C., Wei, Y., Wei, Y., & Wang, H. (2025). Collaboration and Resource Sharing for the Multi-Depot Electric Vehicle Routing Problem with Time Windows and Dynamic Customer Demands. Sustainability (Switzerland), 17(6). https://doi.org/10.3390/su17062700
Yang, L. (2022). Research on Logistics Distribution Vehicle Path Optimization Based on Simulated Annealing Algorithm. Advances in Multimedia, 2022(1), 7363279. https://doi.org/https://doi.org/10.1155/2022/7363279
Zeng, X. (2022). Research on logistics dispatch algorithm based on ant colony optimization neural network. Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers, 1016–1020. https://doi.org/10.1145/3544109.3544401
Zhang, Y., Yuan, Y., & Lu, K. (2019). E-commerce information system data analytics by advanced ACO for asymmetric capacitated vehicle delivery routing. Information Systems and E-Business Management, 18. https://doi.org/10.1007/s10257-019-00405-y
Zheng, L., He, Z., & Liang, W. (2020). VRP Problem Solving Based on Adaptive Dynamic Search Ant Colony Algorithm. Journal of Physics: Conference Series, 1487(1), 0–8. https://doi.org/10.1088/1742-6596/1487/1/012030
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Deni Muhamad Ramdan*, Verani Hartati
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Electronic Journal of Education, Social Economics and Technology (eJESET)
ISSN 2723-6250 (Online)
Published by the SAINTIS Publishing
Homepage: http://ejeset.saintispub.com
Editor E-mail : ; editor.ejeset@gmail.com
Editorial Office Address:
