Optimizing Fuel Distribution Costs through Vehicle Routing Problem Modeling in Jakarta-Tanjung Gerem Terminals
DOI:
https://doi.org/10.37641/jimkes.v13i5.3547Keywords:
Cost Efficiency, Fuel Distribution, Route Optimization, Vehicle Routing ProblemAbstract
Fuel distribution in Indonesia faces significant challenges due to high transportation costs, particularly for land-based deliveries in high-volume urban corridors. This study aims to identify inefficiencies in fuel distribution operations and develop optimized routing strategies for a major energy company operating between Jakarta Integrated Terminal and Tanjung Gerem Fuel Terminal. A mixed-method approach was employed, combining Root Cause Analysis to pinpoint cost drivers and Vehicle Routing Problem models to optimize delivery routes. Data were collected through interviews, on-site observations, and internal company records from January 2025. The findings reveal that truck transportation dominates operational expenses, driven by excessive travel distances. The optimized Vehicle Routing Problem model, using multi-supply point strategies and linear programming, significantly reduces travel distances and operational costs while improving truck utilization. This study concludes that data-driven route optimization enhances cost efficiency and supports environmental sustainability by lowering fuel consumption. However, the model’s reliance on static demand assumptions limits its adaptability to real-time variations. Future research should explore dynamic routing models incorporating real-time traffic and demand data to enhance robustness. These findings offer a scalable framework for improving fuel distribution efficiency across other terminals, contributing to cost savings and sustainable logistics practices.
Downloads
References
Abbasi, K. R., Shahbaz, M., Zhang, J., Irfan, M., & Alvarado, R. (2022). Analyze the environmental sustainability factors of China: The role of fossil fuel energy and renewable energy. Renewable Energy, 187(3), 390–402.
Abdoli, B., MirHassani, S. A., & Hooshmand, F. (2017). Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions. Environmental Science and Pollution Research, 24(27), 21610–21624.
Alinaghian, M., & Naderipour, M. (2016). A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: A case study. Computers & Industrial Engineering, 99(1), 210–222.
Baptista, G., Vieira, M., & Pinto, T. (2024). An exact approach to the multi-compartment vehicle routing problem: The case of a fuel distribution company. Mathematics, 12(4), 527-543.
Braekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99(3), 300–313.
Chikozho, G., Mutambara, D., & Mutambara, E. (2022). Investigating the root causes of poor service delivery in state-owned enterprises: A case of fleet management inefficiencies. Journal of Transport and Logistics, 5(3), 45–62.
Christopher, M. (2016). Logistics and supply chain management (5th ed.). London: Pearson Education Limited.
Cohen, M. A., & Mallik, S. (1997). Supply chain management: Theoretical perspectives and practical challenges. Operations Research, 45(3), 241–250.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91.
Dantzig, G. B., Fulkerson, D. R., & Johnson, S. M. (1954). Solution of a large-scale traveling-salesman problem. Journal of the Operations Research Society of America, 2(4), 393–410.
Efthymiadis, S., Liapis, N., & Nenes, G. (2023). Solving a heterogeneous fleet multi-compartment vehicle routing problem: A case study. International Journal of Systems Science: Operations & Logistics, 10(1), 219-234.
Eydi, A., & Alavi, H. (2019). Vehicle routing problem in reverse logistics with split demands of customers and fuel consumption optimization. Arabian Journal for Science and Engineering, 44(3), 2641–2651.
Gan, Q. (2022). A logistics distribution route optimization model based on hybrid intelligent algorithm and its application. Annals of Operations Research, 308(1), 1–13.
Indrianti, N., Leuveano, R. A. C., Abdul-Rashid, S. H., & Ridho, M. I. (2025). Green vehicle routing problem optimization for LPG distribution: Genetic algorithms for complex constraints and emission reduction. Sustainability, 17(3), 1144-1155.
Jie, H., Khan, I., Alharthi, M., Zafar, M. W., & Saeed, A. (2023). Sustainable energy policy, socio-economic development, and ecological footprint: The economic significance of natural resources, population growth, and industrial development. Utilities Policy, 81(1), 101-111.
Kalt, G., Wiedenhofer, D., Görg, C., & Haberl, H. (2019). Conceptualizing energy services: A review of energy and well-being along the energy service cascade. Energy Research & Social Science, 53(1), 47–58.
Kanwal, S., Mehran, M. T., Hassan, M., Anwar, M., Naqvi, S. R., & Khoja, A. H. (2022). An integrated future approach for the energy security of Pakistan: Replacement of fossil fuels with syngas for better environment and socio-economic development. Renewable and Sustainable Energy Reviews, 156(1), 111-147.
Konstantakopoulos, G. D., Gayialis, S. P., & Kechagias, E. P. (2022). Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification. Operational Research, 22(3), 2033–2062.
Kusuma, R. P., Kurniawati, D. A., Kristanto, D., Yusof, N. M., & Wong, K. Y. (2021). Optimizing distribution route of packed drinking water with the Clarke and Wright savings and nearest neighbor methods (Case study of PT. GSI). Journal of Industrial Engineering and Halal Industries, 2(2), 77–84.
Li, J., Wang, D., & Zhang, J. (2018). Heterogeneous fixed fleet vehicle routing problem based on fuel and carbon emissions. Journal of Cleaner Production, 201(3), 896–908.
Li, Y., Lim, M. K., & Tseng, M. L. (2019). A green vehicle routing model based on modified particle swarm optimization for cold chain logistics. Industrial Management & Data Systems, 119(3), 473–494.
Li, Y., Soleimani, H., & Zohal, M. (2019). An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives. Journal of Cleaner Production, 227(7), 1161–1172.
Liberatore, M. J., & Miller, T. (2016). Outbound logistics performance and profitability: Taxonomy of manufacturing and service organizations. Business and Economics Journal, 7(2), 221-234.
Lim, T. Y., Ng, X. J., Tan, C. J., & Lim, C. P. (2025). Stepwise monogamous pairing genetic algorithm method applied to a multi-depot vehicle routing problem with time windows. Neural Computing and Applications, 37(16), 9639–9668.
Liu, Z., Niu, Y., Guo, C., & Jia, S. (2023). A vehicle routing optimization model for community group buying considering carbon emissions and total distribution costs. Energies, 16(2), 931-949.
Manavizadeh, N., Farrokhi-Asl, H., & Lim, S. F. W. T. (2020). A new mathematical model for the green vehicle routing problem by considering a bi-fuel mixed vehicle fleet. Journal of Optimization in Industrial Engineering, 13(2), 165–183.
Mardiana, R., Siregar, A. P., & Budiman, H. (2013). Trends in fossil fuel consumption and import dependence in Indonesia. International Journal of Energy Research, 37(4), 512–520.
Matijević, L. (2023). Metaheuristic approaches for the green vehicle routing problem with alternative fuel vehicles. Novi Sad: University of Novi Sad.
Ministry of Energy and Mineral Resources. (2023). Handbook of energy and economic statistics of Indonesia 2023. Retrieved on March 6, 2025 from https://esdm.go.id/assets/media/content/content-handbook-of-energy-and-economic-statistics-of-indonesia-2023.pdf
Norouzi, N., Sadegh-Amalnick, M., & Tavakkoli-Moghaddam, R. (2017). Modified particle swarm optimization in a time-dependent vehicle routing problem: Minimizing fuel consumption. Optimization Letters, 11(1), 121–134.
Popović, D., Vidović, M., & Radivojević, G. (2012). Variable neighborhood search heuristic for the inventory routing problem in fuel delivery. Expert Systems with Applications, 39(18), 13390–13398.
Qin, G., Tao, F., & Li, L. (2019). A vehicle routing optimization problem for cold chain logistics considering customer satisfaction and carbon emissions. International Journal of Environmental Research and Public Health, 16(4), 576-593.
Qin, G., Tao, F., Li, L., & Chen, Z. (2019). Optimization of the simultaneous pickup and delivery vehicle routing problem based on carbon tax. Industrial Management & Data Systems, 119(9), 2055–2071.
Ramadhani, D. S., Masruroh, N. A., & Waluyo, J. (2021). Model of vehicle routing problem with split delivery, multi trips, multi products and compartments for determining fuel distribution routes. ASEAN Journal of Systems Engineering, 5(2), 51–55.
Saber, D., Yousfi, N., Selmi, S., Alotaibi, M., Alotaibi, N., Alharthi, R., Alwgdani, H., & Alotaibi, R. (2025). Optimization of last-mile delivery in KSA. Quantum Journal of Engineering, Science and Technology, 6(1), 71–83.
Toth, P., & Vigo, D. (Eds.). (2002). The vehicle routing problem. Bangkok: SIAM – Society for Industrial and Applied Mathematics.
Vidović, M., Popović, D., & Ratković, B. (2014). Mixed integer and heuristics model for the inventory routing problem in fuel delivery. International Journal of Production Economics, 147(3), 593–604.
Wang, S., Tao, F., & Shi, Y. (2018). Optimization of inventory routing problem in refined oil logistics with the perspective of carbon tax. Energies, 11(6), 1437-1447.
Wang, S., Tao, F., Shi, Y., & Wen, H. (2017). Optimization of vehicle routing problem with time windows for cold chain logistics based on carbon tax. Sustainability, 9(5), 694-715.
Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39(7), 1419–1431.
Zhang, J., Zhao, Y., Xue, W., & Li, J. (2015). Vehicle routing problem with fuel consumption and carbon emission. International Journal of Production Economics, 170(1), 234–242.
