Toward Sustainable Smart Last-Mile Logistics: A Machine Learning-Enabled Framework for Adaptive Control and Dynamic Prediction

Food delivery logistics sustainability includes environmental impact, economic efficiency, and service quality. Traditional logistics models mainly rely on fixed “pickup buffer” policies (such as a set 10 min wait). These systems do not account for the changing nature of restaurant operations and delivery conditions, leading to higher operating costs, driver idle time, and poorer food quality. To move delivery systems from reactive decision-making to proactive, dynamically forecasted operations, an adaptive control mechanism is needed. In on-demand food delivery, this offers a clear path to sustainability through better dispatch accuracy, order prep, and pickup coordination. To resolve these bottlenecks, this study examines how a smart logistics framework based on a dynamic Gradient Boosting Regressor (GBR) and policy-sensitive GBR can provide more accurate estimates of drivers’ waiting times in light of contextual factors such as rush hour, time of day, and operational constraints. In last-mile food delivery, the proposed method aims to reduce operational costs, improve scheduling effectiveness, and maximize resource utilization by moving beyond static, predefined waiting periods to adaptive, context-aware decisions. The developed framework analyzes a proprietary dataset of 368,250 instant orders from a major Saudi Arabian logistics provider to evaluate the efficacy of static thresholds versus a proposed predictive, dynamic machine-learning-based approach. After rigorous data cleaning and temporal-logic adjustments, a “High-Fidelity Ground-Truth” subset of 1842 verified orders is used to simulate policy performance. This 99.5% reduction is necessitated by the widespread absence of the “Order Ready” timestamp in operational logs, which is the critical target variable for supervised learning; comparative analysis confirms the subset remains representative of the parent population’s spatiotemporal dynamics. The baseline analysis reveals severe inefficiencies in the static model, with a 61.67% violation rate for driver wait times, particularly in Riyadh ( p < 0.001 ) and during late-night operations. The simulation results demonstrate that the dynamic policy reduces the “Buffer Miss Rate” (premature driver arrivals) from 59.08% to 7.32%, resulting in a 68.5% reduction in total operational waste costs.