Smart Route Optimization for Gas Distribution: A Heuristic Approach to Enhance Efficiency

Efficient vehicle routing is essential for optimizing distribution networks, reducing costs, and improving customer satisfaction. In logistics-intensive industries such as gas distribution, optimizing vehicle routes can lead to substantial cost reductions, improved delivery reliability, and enhanced customer service. This research presents an optimized vehicle routing solution for a gas agency operating in South Asia, addressing inefficiencies in existing delivery routes. Using collected data, the study formulates a Vehicle Routing Problem model and applies heuristic algorithms such as the Nearest Neighbor Algorithm for optimization. The research integrates demand analysis, route mapping, and computational tools to minimize travel distance, fuel consumption, and operational costs. A systematic evaluation of existing routing methods compared to optimized models reveals significant improvements in delivery efficiency, reduced costs, and enhanced customer satisfaction. The findings contribute to the broader field of logistics optimization, demonstrating how heuristic-based solutions can improve resource allocation and supply chain performance. Additionally, the study provides insights into potential future research directions, such as integrating real-time traffic data and machine learning-based optimization techniques to enhance vehicle routing strategies. Distance after optimization shows that the route was about 44% longer before optimization, which is a reasonable to conclude that the path wasn't planned efficiently.