Optimizing multi-objective instant logistics with trucks and drones for the quick commerce order fulfilment

Abstract

Quick commerce (Q-commerce) is an emerging online retail model that demands near-instant order fulfillment and adapts to delivering goods with varying environmental requirements, including temperature-sensitive items. Addressing the complex optimization challenges in distribution, a gap in effective delivery route planning and tool utilization has been identified. This study introduces three innovations: (1) a formulation that reduces drone sub-route path computations for real-time decision-making, (2) a multi-objective instant delivery model employing both vehicles and drones, and (3) a multi-objective memetic algorithm that accounts for time windows and temperature variations. Computational experiments demonstrate that this approach outperforms traditional models, significantly enhancing delivery time satisfaction, product quality, and overall service efficiency. The model scales effectively to large order quantities while maintaining a high customer satisfaction rate of 97%. This research contributes to the industrial engineering literature by presenting a novel Q-commerce logistics model and offering insights into multi-objective optimization for last-mile delivery.