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

Abstract

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.

Keywords:

• Large-scale urban logistics
• sparrow search algorithm
• multiple depot vehicle routing problem
• complex road network

Acknowledgements

We would like to thank the editors and anonymous reviewers for their constructive suggestions and comments for improving this manuscript. We also thank Dr. Yongze Song for his insightful suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of the present study are available on Figshare at https://doi.org/10.6084/m9.figshare.19289150.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [Grant No. 2019YFB2102903], the National Natural Science Foundation of China [Grant No. 41801306 and 42171466], Alibaba Innovative Resarch Project [No. 20228670], the “CUG Scholar” Scientific Research Funds at China University of Geosciences (Wuhan) (Project No. 2022034) and a grant from State Key Laboratory of Resources and Environmental Information System.

Notes on contributors

Yao Yao

Yao Yao is a professor at China University of Geosciences (Wuhan), a researcher from the Center for Spatial Information Science at the University of Tokyo, and a senior algorithm engineer at Alibaba Group. His research interests are geospatial big data mining, analysis, and computational urban science.

Siqi Lei

Siqi Lei is currently a master’s student at China University of Geosciences (Wuhan). Her research interests include geospatial spatial analysis, logistics optimization, and applied artificial intelligence.

Zijin Guo

Zijin Guo is a master’s student at China University of Geosciences (Wuhan). His research interests are trajectory data mining and complex network analysis.

Yuanyuan Li

Yuanyuan Li is currently a Ph.D. candidate at China University of Geosciences (Wuhan). His research interests include geospatial spatial analysis, logistics optimization, and applied artificial intelligence

Shuliang Ren

Shuliang Ren is currently a Ph.D. candidate in GIScience at the Institute of Remote Sensing and Geographical Information Systems,Peking University, Beijing. His research interests are GeoAl, spatial analysis, and urban data mining.

Zhihang Liu

Zhihang Liu is a master’s student at Peking University. He is currently a visiting student at the Technical University of Munich, German. His research interests include Urban Computing, Computational Social Science and Complex Networks.

Qingfeng Guan

Qingfeng Guan is a professor at China University of Geosciences (Wuhan). His research interests are high-performance spatial intelligence computation and urban computing.

Peng Luo

Peng Luo is a Ph.D. candidate at the Chair of Cartography and Visual Analytics at the Technical University of Munich, Germany. His research interests include spatial association modelling, social sensing, and applied artificial intelligence.