AI-Driven Optimization of Last-Mile Delivery in Q-Commerce: A Dispatching Model for Operational Efficiency in Emerging Markets

Abstract

The rapid evolution of Quick Commerce (Q-Commerce) is transforming consumer expectations by prioritizing speed, personalization, and digital integration. This study explores the role of artificial intelligence (AI) in optimizing last-mile delivery processes through the development of an AI-driven dispatching model tailored to the unique challenges of emerging markets. Drawing on real-world data and simulation analysis, the model leverages AI to enhance routing efficiency, reduce delivery times, and improve service reliability. The proposed framework integrates key operational metrics and contextual variables to reflect the complexity of high-velocity delivery environments. Empirical evaluation using simulation scenarios demonstrates that AI-enabled dispatching significantly outperforms traditional methods in terms of responsiveness and resource utilization. These findings offer actionable insights for digital platform managers and logistics providers seeking scalable solutions in rapidly urbanizing regions. The study contributes to the literature by bridging the gap between AI application and practical performance gains in Q-Commerce logistics.