Optimization strategy of snow removal resources allocation before airport disaster

doi:10.16265/j.cnki.issn1003-3033.2025.11.1572

Abstract

Abstract:

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.

Key words: 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)

CLC Number:

X915.5

HUANG Xin, LI Ru, XU Ping, WU Kun. Optimization strategy of snow removal resources allocation before airport disaster[J]. China Safety Science Journal, 2025, 35(11): 56-64.

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参数	取值
f_t	0.50~1.50
f_c	0.95~1.00
C_L/(元·人^-1)	300
S_Q/m²	1 451 016
S_L/m²	72 766
X_ij(i=1,2,3)	下限为(7,4,4) 上限为(10,6,6)
Y_k	[60,100]

单位性质	机场	研究机构	民航高校
占比/%	50	20	30
从业年限/a	<10	10~20	≥20
占比/%	10	50	40

专家序号	恢复力指标	成本指标
1	5	3
2	5	4
3	4	5
4	5	3
5	4	5
6	5	3
7	5	4
8	4	5
9	5	3
10	5	4

标准化		绝对权重值
恢复力指标	成本指标	恢复力指标	成本指标
1.000 0	1.000 0	0.002 0	0.002 0
0.965 0	0.974 4	0.017 0	0.017 1
0.949 5	0.970 7	0.066 8	0.068 3
0.947 5	0.912 6	0.155 5	0.149 7
0.912 5	0.886 8	0.224 6	0.218 2
0.895 9	0.882 8	0.220 5	0.217 2
0.895 0	0.823 5	0.146 8	0.135 1
0.825 0	0.771 4	0.058 0	0.054 2
0.787 3	0.754 3	0.013 8	0.013 3
0.680 7	0.714 3	0.001 3	0.001 4

解的序号	赋权值		解的距离		R_m	排序
解的序号	恢复力指标	成本指标	最优解距离	最劣解距离	R_m	排序
1	0.359 0	0.337 6	0.168 4	0.135 0	0.445 0	10
2	0.527 4	0.472 6	0.135 0	0.168 4	0.55 5 0	6
3	0.499 7	0.431 3	0.097 7	0.146 6	0.600 1	2
4	0.472 0	0.389 2	0.075 7	0.140 5	0.649 8	1
5	0.472 5	0.417 2	0.096 7	0.126 3	0.566 4	5
6	0.500 8	0.458 7	0.124 0	0.142 4	0.534 5	8
7	0.435 1	0.364 6	0.096 1	0.132 1	0.578 9	4
8	0.481 3	0.419 1	0.093 6	0.133 4	0.587 6	3
9	0.509 0	0.460 5	0.124 3	0.150 4	0.547 5	7
10	0.415 2	0.356 5	0.113 8	0.129 0	0.531 4	9