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Research on Layout Optimization and Hybrid Algorithm Improvement of Railway Rescue Trains Based on Arc Risk Quantification
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Jiaying HAN1, 2, Jinghui LIU2, Qiufen LI2, Xingui LIU2, Junwei ZHANG2, Jizhou ZHANG2, Yuchen ZHANG2
China Railway Science | 2026, 47(2) : 221 - 231
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China Railway Science | 2026, 47(2): 221-231
Research on Layout Optimization and Hybrid Algorithm Improvement of Railway Rescue Trains Based on Arc Risk Quantification
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Jiaying HAN1, 2, Jinghui LIU2, Qiufen LI2, Xingui LIU2, Junwei ZHANG2, Jizhou ZHANG2, Yuchen ZHANG2
Affiliations
  • 1.Graduate Department, China Academy of Railway Sciences, Beijing100081, China
  • 2.China Railway Safety Research and Development Center, China State Railway Group Co., Ltd., Beijing100081, China
Published: 2026-03-01 doi: 10.3969/j.issn.1001-4632.2026.02.19
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To optimize the layout of railway rescue trains and enhance railway emergency rescue efficiency in China,the genetic-simulated annealing hybrid algorithm is improved based on the arc risk quantification. First, a multi-dimensional risk quantification evaluation index system for the railway network is constructed. Through the Entropy Weight-TOPSIS method, risk quantification evaluation is conducted on each arc segment of the network. Then, combined with coverage theory, an optimal layout model for railway rescue trains is established with objectives including network rescue coverage rate, rescue time satisfaction, and rescue train layout cost. Secondly, the Multi-Phase Adaptive Simulated Annealing Genetic Algorithm (MP-ASAGA) is designed to solve the model. The solution process is divided into the exploration phase focusing on searching for the global optimum and the development phase focusing on accelerating convergence, with different evolutionary strategies applied in each phase to improve the algorithm's solving performance. Finally, a case study using actual railway network data from a railway bureau in China is conducted for calculation and validation. The results show that compared with the original layout scheme of the railway bureau in the case study, the optimal railway rescue train layout scheme obtained by the proposed method achieves an improvement of 8.99% in network rescue coverage rate, and an improvement of 11.62% in rescue time satisfaction. This method can provide corresponding theoretical support for the layout optimization of railway rescue trains and the enhancement of rescue efficiency.

Railway rescue trains  /  Entropy Weight-TOPSIS method  /  Coverage theory  /  Maximum Covering Location Problem (MCLP)  /  Heuristic algorithm
Jiaying HAN, Jinghui LIU, Qiufen LI, Xingui LIU, Junwei ZHANG, Jizhou ZHANG, Yuchen ZHANG. Research on Layout Optimization and Hybrid Algorithm Improvement of Railway Rescue Trains Based on Arc Risk Quantification[J]. China Railway Science, 2026 , 47 (2) : 221 -231 . DOI: 10.3969/j.issn.1001-4632.2026.02.19
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Year 2026 volume 47 Issue 2
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Article Info
doi: 10.3969/j.issn.1001-4632.2026.02.19
  • Receive Date：2025-07-24
  • Online Date：2026-06-03
  • Published：2026-03-01
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History
  • Received：2025-07-24
  • Revised：2026-02-27
Affiliations
    1.Graduate Department, China Academy of Railway Sciences, Beijing100081, China
    2.China Railway Safety Research and Development Center, China State Railway Group Co., Ltd., Beijing100081, China
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