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Urban logistics is vital to the development and operation of cities, and its optimization is highly beneficial to economic growth. The increasing customer needs and the complexity of urban systems are two challenges for current logistics optimization. However, little research considers both, failing to balance efficiency and cost. In this study, we propose a hybrid sparrow search algorithm (SA-SSA) by combining the sparrow search algorithm with fast computational speed and the simulated annealing algorithm with the ability to get the global optimum solution. Wuhan city was selected for logistics optimization experiments. The results show that the SA-SSA can optimize large-scale urban logistics with guaranteed efficiency and solution quality. Compared with simulated annealing, sparrow search, and genetic algorithm, the cost of SA-SSA was reduced by 17.12, 18.62, and 14.72%, respectively. Although the cost of SS-SSA was 11.50% higher than the ant colony algorithm, its computation time was reduced by 99.06%. In addition, the simulation experiments were conducted to explore the impact of spatial elements on the algorithm performance. The SA-SSA can provide high-quality solutions with high efficiency, considering the constraints of many customers and complex road networks. It can support realizing the scientific scheduling of distribution vehicles by logistics enterprises.

Fast optimization for large scale logistics in complex urban systems using the hybrid sparrow search algorithm