AI-driven Optimization Models for Managing Gas Cylinder Supply Shortages during Geopolitical Conflicts

Abstract

Liquefied Petroleum Gas (LPG) plays a critical role in meeting the domestic energy needs of millions of households in India. The country relies heavily on imported LPG, primarily transported through strategic maritime routes such as the Strait of Hormuz. Geopolitical tensions in the Middle East—particularly conflicts involving Iran and the United States—can significantly disrupt global energy supply chains, leading to shortages, transportation delays, and increased costs in LPG distribution. Such disruptions pose significant challenges to ensuring an uninterrupted household fuel supply, especially during crises. This study proposes an optimization-based framework to address challenges in LPG cylinder distribution under scenarios of supply disruption. A mathematical supply chain model is developed to optimize the allocation and transportation of LPG cylinders from bottling plants to distributors and household clusters. Advanced metaheuristic optimization techniques, including Genetic Algorithm (GA), Tabu Search (TS), and a hybrid GA–Tabu approach, are employed to minimize supply shortages, transportation costs, and delivery delays while maximizing demand satisfaction. Simulation experiments are conducted using representative LPG demand datasets to evaluate the performance of the proposed optimization framework. Results demonstrate that the hybrid GA–Tabu model significantly improves distribution efficiency and reduces cylinder shortages compared with conventional allocation methods. The findings highlight the potential of intelligent optimization techniques for enhancing resilience and sustainability in LPG supply chains during geopolitical disruptions. The proposed model provides a strategic decision-support tool for policymakers and energy logistics planners to ensure reliable household LPG distribution under crisis conditions.