Urban railway logistics node location based on the set covering problem and heuristic algorithm

Abstract

At present, China's total demand for railway construction at home and abroad is at a historically high level. Under the huge construction demand, it is particularly important to reduce the logistics and transportation costs in railway construction. This study aims to reduce the construction cost of logistics bases during railway construction. On the basis of drawing on industry site selection experience and principles, combined with the idea of the set coverage problem, a secondary logistics node site selection model for railway construction is constructed. Based on the heuristic algorithm and Voronoi diagram technology, the optimal solution algorithm of the site selection model is designed. Applying the research algorithm to actual railway construction cases, the optimal solution output showed that the number of logistics points and supply facilities was lower than that of traditional heuristic algorithms. The computation time of the research algorithm was 7.52 ± 1.33 s, which was not significantly different from the computation time of the comparison algorithm. The results indicate that the designed railway site selection model and model optimization algorithm have strong automatic site selection capabilities, which can significantly reduce the construction cost of logistics nodes and have certain application potential.

Keywords

Set coverage problem / Heuristic algorithm / Railway construction / Logistics / Site selection / Voronoi diagram

References

[1] Amann F, Bonsignorio F, Bulik Tet al. . Site-selection criteria for the Einstein Telescope. Review of Scientific Instruments. 2020, 91(9): 094504-094522.

[2] Amann F, Bonsignorio F, Bulik T. Site-selection criteria for the Einstein Telescope. Review of Scientific Instruments. 2020, 91(9): 94504-94522.

[3] Bakioglu G. Enhancing efficiency in railway freight logistics using a two-stage decision support technique with Q-rung orthopair fuzzy sets. Canadian Journal of Civil Engineering. 2025, 52(5): 770-795.

[4] Castañeda FD, García-Acosta G, Garzón-Alvarado DA, Márquez-Flórez K, Quexada-Rodriguez DA, Velasco MA. Design for the additive manufacturing of structural elements with cellular materials using Voronoi diagrams and Delaunay triangulations: Biological and structural applications. Mechanics of Advanced Materials and Structures. 2024, 31(27): 9550-9570.

[5] Chanta S, Sangsawang O, Wiecek MM, Tomii N. A biobjective hub location model with consideration of congestion for railway transportation planning. Journal of Advances in Information Technology. 2024, 15(3): 414-421.

[6] Chary K, Callier MD, Covès D. Scenarios of fish waste deposition at the sub-lagoon scale: A modelling approach for aquaculture zoning and site selection. ICES Journal of Marine Science. 2021, 78(3): 922-939.

[7] Erden C, Ateş Ç, Esen S. Distribution center location selection in humanitarian logistics using hybrid BWM–ARAS: A case study in Türkiye. Journal of Homeland Security and Emergency Management. 2024, 21(3): 383-415.

[8] Feng J. Application of a bilevel programming model in disposal site selection for hazardous waste. Environmental Engineering Science. 2021, 38(8): 789-801.

[9] Hu L, Liu H, Ying C. Construction of hub location model based on genetic algorithm. Transportation Research Record. 2025, 2679(2): 403-420.

[10] Hu Y, Jiang L, Chen K, Li J. Optimized Voronoi diagram path planning based on a ray model. Journal of Mechanical Science and Technology. 2025, 39(1): 353-361.

[11] Kim MJ, Kang TY, Ryoo CK. Real-time path planning for unmanned aerial vehicles based on compensated voronoi diagram. International Journal of Aeronautical and Space Sciences. 2025, 26(1): 235-244.

[12] Koç S, Erden C, Ateş Ç, Ceviz E. Evaluation of potential logistics village alternatives using bayesian best-worst method. Optimality. 2024, 1(1): 100-120

[13] Li K, Atik A, Zheng D, Hajibabai L, Hajbabaie A. A relaxation-based Voronoi diagram approach for equitable resource distribution. Computer-Aided Civil and Infrastructure Engineering. 2025, 40(4): 445-463.

[14] Liu S, Du H, Li G, Gao G, Yang N. Construction of logistics competitiveness system and level measurement of node cities of China Railway Express based on text mining. Current Science. 2025, 5(1): 1079-1090

[15] Mallick J. Municipal solid waste landfill site selection based on Fuzzy-AHP and geoinformation techniques in Asir Region Saudi Arabia. Sustainability. 2021, 13(3): 1538-1542.

[16] Nguyen TMH, van Binsbergen A, Dinh CT. Dry port location selection for integration with inland waterway transport in developing countries: A case study in Northern Vietnam. Journal of Supply Chain Management Science. 2024, 5(1-2): 1-29.

[17] Ou Q, Gan M, An M, Wang Y. Road-rail intermodal hubs site selection based on road freight demand mining–A case from Beijing-Tianjin-Hebei region. Promet-Traffic & Transportation. 2024, 36(3): 492-507.

[18] Şahin M. Ensemble decision making for logistics center location. Environment, Development and Sustainability. 2024, 26(12): 30527-30561.

[19] Tang T. Site selection of fire stations in large cities based on actual spatiotemporal demands: A case study of Nanjing City. ISPRS International Journal of Geo-Information. 2021, 10(8): 542-551.

[20] Wei H, Li A. An optimal study on logistics network of cross-border railway in inland areas considering time cost and benefit of scale of railway transportation. International Journal of Shipping and Transport Logistics. 2024, 18(1): 63-91.

[21] Zhao L, Jiang Y, Li H, Sun S, Yang P, Zhou L. Optimizing logistics hub selection in the integrated network of the China-Europe Railway Express and New International Land-Sea Trade Corridor. Journal of Industrial and Management Optimization. 2025, 21(6): 4384-4411.

RIGHTS & PERMISSIONS

The Author(s)