Framework for truck–RPAS hybrid models in last-mile delivery

This study develops a hybrid optimization framework integrating remotely piloted aircraft systems (RPASs) with conventional truck delivery networks to enhance last-mile logistics efficiency. To balance operating cost, service time, regulatory risk, and energy usage, a novel multi-objective mixed-integer linear programming model is developed. High-quality Pareto-optimal solutions are produced by the non-dominated sorting genetic algorithm II, which methodically manages trade-offs between the conflicting goals. Risk assessment is embedded using specific operations risk assessment principles, and energy consumption is optimized through dynamic battery management strategies for RPASs. Extensive computational experiments demonstrate that the proposed hybrid truck–RPAS system achieves notable operational improvements compared to traditional truck-only models. The model yields an 8.3% reduction in operational costs, an 8.6% decrease in delivery time, an 11.2% reduction in cumulative risk indices, and a 9.4% decrease in overall battery usage. Convergence analysis and scalability evaluation further confirm the robustness and practical viability of the proposed solution approach. By integrating regulatory compliance, energy sustainability, and operational resilience, this research provides a scalable and adaptable framework for the effective deployment of RPAS technologies in urban logistics systems, addressing key challenges of modern supply chains and supporting future sustainable transportation initiatives.