收藏切换
Optimization strategy of snow removal resources allocation before airport disaster
收藏切换
PDF
Xin HUANG1, Ru LI1, Ping XU1, 2, Kun WU1
China Safety Science Journal | 2025, 35(11) : 56 - 64
Less
收藏切换
China Safety Science Journal | 2025, 35(11): 56-64
Safety engineering technology
Optimization strategy of snow removal resources allocation before airport disaster
Full
Xin HUANG1, Ru LI1, Ping XU1, 2, Kun WU1
Affiliations
  • 1School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
  • 2Bazhong Enyang Airport, Bazhong Sichuan 636066, China
Published: 2025-11-28 doi: 10.16265/j.cnki.issn1003-3033.2025.11.1572
Outline
收藏切换

To improve the efficiency of pre-disaster airport snow removal resource allocation, collaborative operations between equipment and artificial snow removal were considered. Snow removal resilience was introduced to represent snow removal efficiency, and a multi-objective optimization analysis model of airport pre-disaster snow removal resource reserve was established. The NSGA-Ⅱ was used to solve the optimal solution set and Pareto frontier. The C-OWA operator was introduced to calculate the objective weights of snow removal resilience and cost, and composite weight was determined by combining the subjective weight. Based on the TOPSIS method, the best scheme of pre-disaster snow removal resource reserve was obtained, and the influence of the subjective weight assigned to snow removal resilience on the decision results was analyzed. The results show that the maximum and minimum resilience of the optimal solution set of the multi-objective optimization model considering resilience and cost are 108 600 and 93 928 m3/h, respectively, and the costs are 1.118 million yuan and 798 600 yuan, respectively. The snow removal resilience of the best solution in the optimal solution set of pre-disaster snow removal resource reserve is 97 200 m3/h, and the snow removal cost is 920 700 yuan. Compared with the optimal solution set, the resilience of the optimal solution obtained by optimization analysis is increased by 31.5%, indicating that the established pre-disaster snow removal resource allocation method is feasible. Snow removal resilience is positively correlated with the subjective weight. For example, when the subjective weight is 0, 0.2 and 0.4, the corresponding snow removal resilience are 85 496, 89 600 and 97 200 m3/h, respectively.

airport snow removal resource allocation  /  multi-objective optimization  /  non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)  /  continuous ordered weighted averaging operator (C-OWA operator)  /  technique for order preference by similarity to ideal solution (TOPSIS)
Xin HUANG, Ru LI, Ping XU, Kun WU. Optimization strategy of snow removal resources allocation before airport disaster[J]. China Safety Science Journal, 2025 , 35 (11) : 56 -64 . DOI: 10.16265/j.cnki.issn1003-3033.2025.11.1572
Year 2025 volume 35 Issue 11
PDF
33
6
Cite this Article
BibTeX
Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.11.1572
  • Receive Date:2025-06-16
  • Online Date:2026-07-09
  • Published:2025-11-28
Article Data
Affiliations
History
  • Received:2025-06-16
  • Revised:2025-09-21
Funding
Affiliations
    1School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
    2Bazhong Enyang Airport, Bazhong Sichuan 636066, China
References
Share
https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2025.11.1572
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT