Research on dual objective optimization of location and allocation for urban logistics drone distribution centers based on cost and timeliness

Abstract This study proposes an innovative dual-objective optimization model for the location and allocation problem of drone delivery centers in urban logistics, aiming to simultaneously minimize the overall cost and maximize delivery time efficiency. To address the complexity of this NP-hard problem, an Improved Genetic-Particle Swarm Optimization (IGA-PSO) hybrid algorithm was designed, which integrates a simulated annealing selection strategy and a Gaussian noise perturbation mechanism to enhance solution quality and robustness. Simulation results based on a 50-node urban scenario demonstrate that the proposed IGA-PSO significantly outperforms standard GA and PSO, converging to the optimal solution (Total Cost: 300,704 CNY; Average Time Utility: 0.894) by the 20th iteration. Sensitivity analysis further reveals the critical impact of airspace configurations: expanding feasible airspace reduces total costs by 15.4% (to 254,438 CNY), whereas introducing new no-fly zones increases costs by 9.6%. Additionally, scalability tests confirm that the model maintains linear time complexity ( $${R}^{2}\approx0.99$$ ) as the number of demand nodes increases. This study provides theoretical support and data-driven guidance for the coordinated optimization of economy, efficiency, and sustainability in urban drone logistics systems.