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All Journal International Journal of Advances in Intelligent Informatics
Abdul-Rashid, Salwa Hanim
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Computer Science & IT

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Optimizing LPG distribution: A hybrid particle swarm optimization and genetic algorithm for efficient vehicle routing and cost minimization Indrianti, Nur; Leuveano, Raden Achmad Chairdino; Abdul-Rashid, Salwa Hanim; Kuncoro, Andreas Mahendro; Liestyana, Yuli
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1837

Abstract

This paper aims to develop an optimized solution for the Vehicle Routing Problem (VRP), tailored explicitly for Liquid Petroleum Gas (LPG) distribution, with a focus on minimizing transportation costs and enhancing delivery reliability. The critical role of LPG as an essential public infrastructure commodity, widely utilized for cooking and heating, makes its efficient and reliable distribution a significant logistical challenge due to the strict adherence to delivery time windows, heterogeneous fleets, multi-trip scenarios, and intricate loading and unloading requirements. To address these complexities, this study proposes a novel hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) that uniquely integrates multi-trip routing, time windows, and heterogeneous vehicle fleet management into a single optimization framework. The dual-phase optimization strategy leverages the exploratory capability of PSO and the solution-refining power of GA, resulting in high-quality, feasible solutions. Validation against real-world data involving VRP instances with 88 and 40 stations demonstrates the model’s practical impact, achieving reductions of up to 4.56% in transportation costs compared to existing operational routes. This research makes a significant contribution to interdisciplinary domains, including logistics optimization, sustainability, and energy distribution, by offering a robust and scalable model that comprehensively addresses complex, real-world VRP constraints.
Co-Authors Kuncoro, Andreas M Leuveano, Raden Achmad Chairdino Nur Indrianti Yuli Liestyana