Optimizing urban last mile delivery efficiency through dynamic vehicle routing heuristics and traffic flow analysis

Urban last-mile delivery systems operate under volatile traffic conditions, narrow delivery windows, and frequent real-time disruptions that limit the effectiveness of static and single-strategy vehicle routing approaches. Existing dynamic and traffic-aware VRP models address these challenges only partially, lacking unified mechanisms for adaptive search, congestion-sensitive cost evaluation, and proactive disruption management. This research introduces a Tabu-guided Adaptive Large Neighborhood Search with Rollout-based Real-Time Dispatch (T-ALNS-RRD), a traffic-aware optimization framework that integrates three previously uncombines components: (a) an ALNS core enhanced with dynamic congestion-penalized cost functions, (b) a multi-layered Tabu memory system with move-based, solution-based, and frequency-based diversification for non-cycling exploration, and (c) a rollout-based dispatch mechanism that conducts bounded-horizon simulations to pre-emptively select disruption responses. Experimental evaluation on a realistic urban delivery scenario involving 47 customers and 4 vehicles demonstrates significant performance advances over static and traffic-aware baselines. T-ALNS-RRD achieves a 24.3% reduction in total operational cost, increases on-time delivery rates from 68.1% to 92.8%, and reduces congestion exposure by 54.4%. Under extreme traffic variability, the method limits degradation to 15.1%, compared to 26.2% in static systems. Real-time adaptation enables the successful handling of 27.4 disruptive events per scenario with a 94.2% resolution rate and an average response time of 143.7 ms. Statistical comparison against state-of-the-art (SOTA) metaheuristics confirms a performance improvement of 6.6% (p < 0.001) under the tested conditions. These findings establish the approach as a validated mid-scale framework for dynamic urban delivery optimization and provide a scalable methodological basis for future large-instance deployments.