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.
Hidayat et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: