Pendekatan Terpadu pada Perancangan Rute Distribusi untuk Meminimasi Biaya Transportasi
DOI:
https://doi.org/10.35194/jmtsi.v9i2.5663Keywords:
Greedy Gropping Heuristic, Insertion Heuristic, Fuzzy C-Means, Tabu Search Algorithm, P-MedianAbstract
An integrated approach to distribution route design considers several aspects of the distribution network simultaneously such as customer distribution grouping, warehouse locations, routes and vehicle capacity. This approach aims to optimize these factors as an integrated system so that vehicle travel routes are shorter and more efficient and save overall costs. For this purpose, the use of complex mathematical models in designing distribution routes is unavoidable. Distribution route design begins with grouping retailers/agents so that transport vehicles in only one trip can complete their tasks using the Fuzzy c-Means Clustering approach. The next step is the selection of warehouse locations that consider the distribution of demand from retailers/agents in each cluster. For warehouse location selection that is centered on the distribution of retail/agent demand, geographic analysis is very important to map areas with high demand concentrations, so that placing warehouses close to the majority of customers can speed up delivery times and minimize transportation costs. This step uses the P-Median approach with the Greedy Dropping Heuristic Algorithm. This design ends by determining the optimization route using Insertion Heuristic or Vehicle Routing Problem in each cluster, starting with the vehicle departing from the warehouse providing service until all retailers/agents in the cluster are served and returning to the warehouse in just one iteration. The next approach, Tabu Search metaheuristic, is carried out to see other possible shortest distance alternatives by optimizing the shortest distance obtained in the previous stage. This design is applied to the 3 kg gas distribution route in East Bandung City which has six intermediary warehouses and 72 retailers/agents. The integrated design of the 3 kg distribution route produces a cost efficiency of 72.28%. Pendekatan terpadu terhadap perancangan rute distribusi mempertimbangkan sejumlah aspek jaringan distribusi secara bersamaan seperti pengelompokan sebaran pelanggan, lokasi Gudang, rute dan kapasitas kendaraan. Pendekatan ini bertujuan mengoptimalkan faktor-faktor tersebut sebagai satu sistem yang terintegrasi sehingga rute tempuh kendaraan lebih pendek dan efisien serta menghemat biaya keseluruhan. Untuk tujuan ini, penggunaan model matematika kompleks dalam merancang rute distribusi tidak dapat dihindari. Desain rute distribusi diawali dengan mengelompokkan ritel/agen agar kendaraan angkut dalam hanya satu ritasi dapat menyelesaikan tugasnya dengan pendekatan Fuzzy C-Means Clustering. Langkah berikutnya adalah pemilihan lokasi gudang yang mempertimbangkan sebaran permintaan daripada ritel/agen di setiap klaster. Untuk pemilihan lokasi gudang yang berpusat pada sebaran permintaan ritel/agen, analisis geografis sangat penting untuk memetakan area dengan konsentrasi permintaan tinggi, sehingga menempatkan gudang dekat dengan mayoritas pelanggan dapat mempercepat waktu pengiriman dan meminimalkan biaya transportasi. Langkah ini menggunakan pendekatan P-Median dengan Greedy Dropping Heuristic Algorithm. Perancangan ini diakhiri dengan menetapkan rute optimasi menggunakan Insertion Heuristik atau Vehicle Routing Problem pada setiap klaster dengan diawali kendaraan berangkat dari gudang memberi pelayanan hingga semua ritel/agen dalam klaster terlayani dan kembali ke gudang hanya dalam satu ritasi. Pendekatan berikutnya metaheuristik Tabu Search dilakukan untuk melihat kemungkinan alternatif jarak terpendek lainnya dengan melakukan optimisasi jarak terpendek yang telah diperoleh pada tahapan sebelumnya. Perancangan ini diterapkan pada rute distribusi gas 3 kg Kota Bandung Timur yang mempunyai enam gudang perantara dan 72 ritel/agen. Perancangan terpadu rute distribusi 3 kg menghasilkan efisiensi biaya sebesar 72,28%.References
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[2] Plenert, Gerhard J. Supply Chain Optimization Through Segmentation and Analytics. Taylor & Francis Group. New York. 2014.
[3] Grant, David B., Alexander Trautrims dan Chee Yew Wong. Sustainable Logistics and Supply Chain Management. Revised Edition. Kogan Page. London. 2015.
[4] Roe, Michael, Wei Xu dan Dongping Song. Optimazing Supply Chain Performance. Palgrave Mac Millan. United Kingdom. 2015.
[5] Wu, Z., Song, S., & Zhang, Y. (2024). Research on integrated optimization of urban logistics cargo loading and transportation paths. 35. https://doi.org/10. 1117/12.3054512
[6] Stupnikova, E. (2024). Efficient management of material flows in retail chains. Othody Resursy, 11(4). https://doi.org/10.15862/ 08ecor424
[7] Hu, S., Wu, H., Yin, X., Ke, H., & Zou, F. (2023). Logistics and Distribution Path Optimization Model Based on Hybrid Intelligent Algorithm. 1–6. https://doi.org/ 10.1109/ddp60485.2023.00011
[8] Kolla, D. P. (2024). Integration and Optimization of Route Planning and Inventory Management in Supply Chain Transaction Systems: A Comprehensive Analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/ 10.32628/cseit241061173
[9] Ya?mur, E., & Kesen, S. E. (2023). Integrated production scheduling and vehicle routing problem with energy efficient strategies: Mathematical formulation and metaheuristic algorithms. Expert Systems With Applications. https://doi.org/10.1016/j.eswa.2023. 121586
[10] Sun, X., Yu, S., & Song, S. (2024). Research on Optimization of Logistics and Distribution Paths Based on Improved Ant Colony Algorithm. 1705–1710. https://doi.org/10. 1109/icpics62053.2024.10797158
[11] Almeida, C. F., Yamashita, Y., Cools, M., Marchal, J., & Piette, B. (2021). Methodology Focused on Identifying Variables Necessary to Develop Logistics Clusters. Journal of Sustainable Development, 14(3), 147. https://doi.org/ 10.5539/JSD.V14N3P17
[12] Nadhila, A. A., Ariyanto, Y., & Syaifudin, Y. W. (2024). Clusterization of MSMe and Warehouse Locations for Efficiency of Courier Placement. Journal of Evrimata, 144–149. https://doi.org/10.70822/journal ofevrmata. v2i02.66
[13] Xu, C., & Zheng, F. (2024). Research on the Application of Weighted Distance K-means Clustering Algorithm with Capacity Constraint in Express Service Location. 1–5. https://doi.org/10.1109/isctech63666. 2024. 10845541
[14] Mohanty, M., Singh, R., & Shankar, R. (2018). Improving the operational efficiency of outbound retail logistics using clustering of retailers and consumers. Journal of Modelling in Management, 13(3), 646–674. https://doi.org/10.1108/JM2-12-2016-0137
[15] Ulu, M. (2024). Warehouse location selection with fuzzy c-means method. Yönetim ve Ekonomi Ara?t?rmalar? Dergisi, 21(4), 102–114. https://doi.org/10.11611/yead. 1373617
[16] Pajic, V., Andrejic, M., Jolovi?, M., & Kilibarda, M. (2024). Strategic Warehouse Location Selection in Business Logistics: A Novel Approach Using IMF SWARA–MARCOS—A Case Study of a Serbian Logistics Service Provider. Mathematics. https://doi.org/ 10.3390/math12050776.
[17] Klar, R., Andersson, A., Fredriksson, A., & Angelakis, V. (2024). Container Relocation and Retrieval Tradeoffs Minimizing Schedule Deviations and Relocations. IEEE Open Journal of Intelligent Transportation Systems, 1. https://doi.org/10.1109/ojits. 2024.3413197
[18] Krynke, M. (2024). Virtual Simulation Modeling as a Key Element of Warehouse Location Optimization Strategy. Management Systems in Production Engineering, 32(3), 339–344. https://doi. org/10.2478/mspe-2024-0032
[19] Li, X. (2017). Solving transportation problems with warehouse locations based on greedy algorithm. 693–697. https://doi.org/10.2991/AMMEE-17.2017. 133
[20] Zapata, A. C., Garnica, E. A., Mota, R., Álvarez, F., Flores, J. L., & Partida, D. (2020). Warehouse Relocation of a Company in the Automotive Industry Using P-median. Advances in Science, Technology and Engineering Systems Journal, 5(3), 576–582. https://doi.org/ 10.25046/AJ050 372
[21] Karoonsoontawong, A., Kobkiattawin, O., Xie, C., & Xie, C. (2019). Efficient Insertion Heuristic Algorithms for Multi-Trip Inventory Routing Problem with Time Windows, Shift Time Limits and Variable Delivery Time. Networks and Spatial Economics, 19(2), 331–379. https://doi.org/10.1007/ S11067-017-9369-7
[22] Fernando, W., Thibbotuwawa, A., Perera, H. N., Nielsen, P., & Kilic, D. K. (2024). An integrated vehicle routing model to optimize agricultural products distribution in retail chains. Cleaner Logistics and Supply Chain. https://doi.org/10.1016/j. clscn.2023.100137
[23] Hlaing, W. M., Liu, S.-J., & Pan, J.-S. (2019). A novel solution for simultaneously finding the shortest and possible paths in complex networks. Journal of Internet Technology, 20(6), 1693–1707. https://jit. ndhu.edu.tw/article/download/2158/2171
[24] Reddy, T. S., Dhanush, D., Krithin, T., & Jayan, S. (2024). Supply Chain Logistics With Hybrid Optimization using ADMM and Vehicle Routing Problem. 1–7. https://doi.org/10.1109/icicec62498.2024. 10808286
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[26] Saragih, N. I., & Turnip, P. (2024). Tabu Search Algorithm for Solving a Location-Routing-Inventory Problem. Spektrum Industri?: Jurnal Ilmiah Pengetahuan Dan Penerapan Teknik Industri, 22(2), 155–162. https://doi.org/10.12928/si.v22i2.234
[27] Ru, S. (2024). Vehicle logistics intermodal route optimization based on Tabu search algorithm. Dental Science Reports, 14. https://doi.org/10.1038/s41598-024-60361-7
[28] Trisolvena, M. N., Wattimena, F. Y., & Untajana, P. P. (2024). Logistics Efficiency in Product Distribution with Genetic Algorithms for Optimal Routes. International Journal Software Engineering and Computer Science. https://doi.org/10.35870/ijsecs. v4i1.2045
[29] Luo, F. (2024). Study on the Impact Mechanism of Supply Chain Integration on Supply Chain Resilience. https://doi.org/10.62051/ wt7hx264
