Urban logistics struggles with fluctuating e-commerce delivery demands. This research introduces an adaptive dispatch algorithm combining computational intelligence with operational flexibility to tackle dynamic routing challenges. three-layer structure guides operations: strategic planning, tactical adjustment, and operational execution. Adaptive tabu search switches between incremental updates for normal operations and major reconfigurations during disruptions. Parallel computing threads maintain speed while aspiration criteria and network evaluation preserve solution quality. Results outperform static optimization significantly. Costs dropped 14.2%; high-variability scenarios improved 21.8%. Processing stayed efficient—86.2% of requests completed within one second, with 88.7% on-time delivery. The system scaled near-linearly to 500 customers, quality declining just 9.7% at maximum capacity. This demand-adaptive method surpasses conventional traffic-based routing for last-mile delivery, offering practical solutions for modern logistics networks. These findings offer valuable insights for enhancing urban delivery systems facing increasingly dynamic demand patterns and operational constraints in modern logistics networks.

Sichong Huang. Real-Time Adaptive Dispatch Algorithm for Dynamic Vehicle Routing with Time-Varying Demand. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 9: 108-118. https://doi.org/10.25236/AJCIS.2025.080914.

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