AI-OPTIMIZED TIME-SLOT DELIVERY FOR EFFICIENT PARCEL DISTRIBUTION IN E- COMMERCE LOGISTICS

Abstract: Last-mile parcel delivery is critically affected by uncertain travel times, metropolitan traffic volatility, fluctuating customer availability, spatial delivery density, courier vehicle capacities, and avoidable failed handover attempts. Conventional dispatching frameworks assign rigid, excessively broad time windows and rely on static travel- time matrices, inevitably resulting in prolonged customer waiting friction, unpunctual courier arrivals, inefficient vehicle capacity utilization, and costly repeated delivery attempts. To resolve these operational bottlenecks, this paper proposes an AI-optimized time-slot delivery framework that seamlessly couples supervised machine-learning arrival prediction with constrained time-window-aware parcel assignment and combinatorial route optimization. Historical delivery telemetry is systematically extracted into multidimensional predictive features, encompassing Haversine transit distances, historical traffic congestion indices, service dwell durations, parcel priority rankings, customer preference distributions, and neighborhood delivery densities. A supervised regression model predicts expected arrival times alongside calibrated handover success probabilities for candidate delivery intervals. Subsequently, a multi-objective optimization engine assigns parcels to feasible time slots and optimal delivery tours while strictly enforcing vehicle payload boundaries, courier shift availability, committed delivery intervals, and operational transit costs. The proposed framework is empirically benchmarked utilizing a multi-zone simulated e-commerce logistics dataset. The experimental evaluations compare a conventional fixed-slot baseline against the proposed AI-assisted paradigm across on-time delivery rates, mean delivery delay, failed attempt frequency, fleet transit mileage, and time-slot utilization efficiency. Simulation results demonstrate that integrating predictive arrival modeling with combinatorial optimization elevates on- time punctuality from 86.0% to 93.4%, reduces average delivery delay by 41.2%, cuts failed deliveries by 34.6%, and shortens fleet transit distance by 11.1%. These findings confirm that decoupled predictive-optimization architectures establish a scalable, robust foundation for next-generation e-commerce parcel logistics.

Keywords: Artificial intelligence, Last-mile delivery, Time-slot prediction, Parcel delivery, Machine learning, Vehicle routing with time windows (VRPTW), Logistics optimization, Supervised regression, Punctuality.