Determining Ice Block Distribution Routes for Fishermen in Bone Regency Using the Nearest Neighbors Algorithm Vehicle Routing Problem

Fahri Anwar; Andi Muhammad Irfan; Tito Bisma May Willis; Sudarmanto Jayanegara; Achmad Romadin

doi:10.55927/fjsr.v4i11.727

Authors

Fahri Anwar Universitas Negeri Makassar
Andi Muhammad Irfan Universitas Negeri Makassar
Tito Bisma May Willis Institut Teknologi Kalimantan
Sudarmanto Jayanegara Universitas Negeri Makassar
Achmad Romadin Universitas Negeri Makassar

DOI:

https://doi.org/10.55927/fjsr.v4i11.727

Keywords:

Distribution Optimisation, Fleet AI, Route Efficiency

Abstract

Indonesia has the potential to produce quality marine fish, one of which is in Bone Regency. There are 10 sub-districts that have coastal areas with a total of 63 villages, which are divided into 3,301 business units that also develop from this sector. This has led to a surge in demand for ice blocks. The purpose of this research is to determine the route that must be travelled by the Ice transport truck. This research determines the optimal route for block ice distribution in Bone Regency by considering demand data, distance travelled, fleet capacity, and operational constraints such as ice durability and planning horizon. The results showed that block ice distribution scheduling in Bone Regency needs to be optimised by reducing route duplication, adding fleets during high season, and utilising advanced optimisation methods such as Genetic Algorithm (GA) and big data to predict demand patterns. The use of AI-based fleet management systems and integration of multi-location distribution networks will increase efficiency, reduce costs, and fulfil demand on time

References

Arvianto, A., Nartadhi, R. L., Sari, D. P., & Budiawan, W. (2018). Penerapan Simulasi Dan Reliabilitas Pada Model Vehicle Routing Problem (Vrp) Dengan Permintaan Probabilistik. Jurnal Simetris, 9(1).

Cook, S. (2003). The importance of the P versus NP question. Journal of the ACM, 50(1), 27–29. https://doi.org/10.1145/602382.602398

Hutasoit, C. S., Susanty, S., & Imran, A. (2014). Penentuan Rute Distribusi Es Balok Menggunakan Algoritma Nearest Neighbour dan Local Search (Studi kasus di PT X). Reka Integra, 02(02), 268–276.

Kurniani, I. H., & Nurhidayat, A. E. (2022). Jurnal Optimasi Teknik Industri Analisis Sistem Pergudangan dengan Meode Fuzzy Tsukamoto dan Fuzzy C-Mean Studi Kasus PT . Kemindo Parama Mandiri. Jurnal Optimasi Teknik Industri, 18–25.

Pamungkas, P., Zahabiyah, R., & Shabrina, N. (2023). Electric Vehicle Routing Problem Using Adaptive Simulated Annealing. Journal of Engineering and Management in Industrial System, 11(1), 35–45. https://doi.org/10.21776/ub.jemis.2023.011.01.4

Paolo Toth & Daniele Vigo, Nedregård, I., Gurobi, O., Pimpler, E., LAFLAQUIERE, J., Sundar, U. M., Yang, C., Cordeau, J. F., Häll, C. H., Andersson, H., Lundgren, J. T., Värbrand, P., Posada, M., Andersson, H., & Häll, C. H. (2006). V ehicle R outing. In Public Transport (Vol. 4, Nomor 1–2).

Poonthalir, G., & Nadarajan, R. (2019). Green vehicle routing problem with queues. Expert Systems with Applications, 138, 112823. https://doi.org/10.1016/j.eswa.2019.112823

Prabowo, F., Imran, A., Prassetiyo, H., & Pendahuluan, I. (2023). Jurnal Optimasi Teknik Industri Penentuan Rute Distribusi Menggunakan Metode Savings Matrix , Nearest Neighbor , dan 2-Opt pada CV X. Jurnal Optimasi Teknik Industri, 47–52.

Wang, Z., & Wen, P. (2020). Optimization of a low-carbon two-echelon heterogeneous-fleet vehicle routing for cold chain logistics under mixed time window. Sustainability (Switzerland), 12(5). https://doi.org/10.3390/su12051967

Wicaksono, P. A., Puspitasari, D., Ariyandanu, S., & Hidayanti, R. (2020). Comparison of Simulated Annealing, Nearest Neighbour, and Tabu Search Methods to Solve Vehicle Routing Problems. IOP Conference Series: Earth and Environmental Science, 426(1). https://doi.org/10.1088/1755-1315/426/1/012138