机场灾前除雪资源配置优化策略

doi:10.16265/j.cnki.issn1003-3033.2025.11.1572

摘要/Abstract

摘要： 为提升灾前机场除雪资源配置效率,考虑机场设备除雪和人工除雪协同作业,引入除雪恢复力考虑除雪效率,建立机场灾前除雪资源配置多目标优化分析模型,采用非支配排序遗传算法Ⅱ(NSGA-Ⅱ)求解得到最优解集和Pareto前沿,引入连续有序加权平均(C-OWA)算子计算除雪恢复力和成本的客观权重,并结合主观权重确定综合权重,基于优劣解距离法(TOPSIS)分析得到灾前除雪资源配置的最佳方案,并分析除雪恢复力的主观权重对决策结果的影响。结果表明:考虑恢复力和成本的多目标优化模型的最优解集的最大和最小恢复力分别为108 600和93 928m³/h,成本分别为111.8万元和79.86万元,灾前除雪资源配置最优解集中最佳方案的除雪恢复力为97 200m³/h,除雪成本为92.07万元,相对最优解集而言,采用优化分析得到的最优方案的恢复力增加了31.5%,说明建立的灾前除雪资源配置方法可行;除雪恢复力与主观权重呈正相关关系,如主观权重为0、0.2和0.4时对应的除雪恢复力分别为85 496、89 600和97 200 m³/h。

关键词: 机场除雪资源配置, 多目标优化, 非支配排序遗传算法Ⅱ(NSGA-Ⅱ), 连续有序加权平均算子(C-OWA算子), 优劣解距离法(TOPSIS)

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)

中图分类号:

X915.5

黄信, 李茹, 徐平, 吴堃. 机场灾前除雪资源配置优化策略[J]. 中国安全科学学报, 2025, 35(11): 56-64.

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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参考文献 18

[1]	刘状壮, 季鹏宇, 张有为, 等. 基于可视化文献计量分析的道路除冰雪技术研究进展[J]. 冰川冻土, 2023, 45(3):1180-1194. doi: 10.7522/j.issn.1000-0240.2023.0089
	LIU Zhuangzhuang, JI Pengyu, ZHANG Youwei, et al. Research progress of road snow and ice removal technology based on visualized bibliometric analysis[J]. Journal of Glaciology and Geocryology, 2023, 45(3):1180-1194. doi: 10.7522/j.issn.1000-0240.2023.0089
[2]	丁东, 李智, 刘丹, 等. 严寒地区列车车体抗冰除雪措施研究综述[J]. 铁道科学与工程学报, 2022, 19(12):3798-3805.
	DING Dong, LI Zhi, LIU Dan, et al. Review on anti-ice and snow removal measures of train body in severe cold region[J]. Journal of Railway Science and Engineering, 2022, 19(12):3798-3805.
[3]	欧阳鹏, 李豫. 严寒地区动车组融冰除雪设施配置探讨[J]. 铁道标准设计, 2025, 69(4):199-204,228.
	OUYANG Peng, LI Yu. Discussion on configuration of ice melting and snow removal facilities for electric multiple units in severe cold regions[J]. Railway Standard Design, 2025, 69(4):199-204,228.
[4]	谢秉磊, 李颖, 刘敏. 带临时补充点的融雪剂撒布车辆路径问题[J]. 系统工程理论与实践, 2014, 34(6):1593-1598. doi: 10.12011/1000-6788(2014)6-1593
	XIE Binglei, LI Ying, LIU Min. Vehicle routing problem with temporary supplementary points for spreading deicing salt[J]. Systems Engineering-Theory & Practice, 2014, 34(6):1593-1598.
[5]	王晶, 刘昊天, 朱建明. 基于韧性城市视角的冰雪天气下路网恢复问题研究[J]. 中国管理科学, 2018, 26(3):177-187.
	WANG Jing, LIU Haotian, ZHU Jianming. Study on road network recovery under snow and ice conditions based on the perspective of resilient city[J]. Chinese Journal of Management Science, 2018, 26(3):177-187.
[6]	KARAA M, GHAZZAI H, MASSOUD Y, et al. A computer vision-based framework for snow removal operation routing[J]. IEEE Open Journal of Circuits and Systems, 2024, 5: 81-91. doi: 10.1109/OJCAS.2023.3326274
[7]	NGUYEN P H D, TRAN D. Optimizing snow and ice route removal operations using vehicle routing problems and geographic information system[J]. Journal of Cold Regions Engineering, 2024, 38(2): DOI: 10.1061/JCRGEI.CRENG-721.
[8]	邢志伟, 李斯, 罗谦. 机场道面除冰雪车辆队形控制模型[J]. 交通运输工程学报, 2019, 19(4):182-190.
	XING Zhiwei, LI Si, LUO Qian. Formation control model of airport pavement deicing vehicles[J]. Journal of Traffic and Transportation Engineering, 2019, 19(4):182-190.
[9]	谭忆秋, 张驰, 陈凤晨, 等. 基于热管技术的机场道面融雪性能试验研究[J]. 中国公路学报, 2019, 32(4):137-147. doi: 10.19721/j.cnki.1001-7372.2019.04.011
	TAN Yiqiu, ZHANG Chi, CHEN Fengchen, et al. Snow melting performance experimental study of airport pavement with heat pipe technology[J]. China Journal of Highway and Transport, 2019, 32(4):137-147. doi: 10.19721/j.cnki.1001-7372.2019.04.011
[10]	ZHANG Yaping, ZHANG Yu, XING Xiaoqing, et al. The method of division and classification quantification for snow and ice removal on airport pavement[C]. Proceedings of the 11^th International Conference on Green Intelligent Transportation Systems and Safety, 2020: 649-664.
[11]	WANG Liwen, YAO Guopeng, WEI Jianglong, et al. Design of airport snow removal operation optimization and dispatch system[J]. Applied Mechanics and Materials, 2011, 52-54:54-58. doi: 10.4028/www.scientific.net/AMM.52-54
[12]	黄信, 谭成松, 吴堃, 等. 暴雪灾害下机场基础设施韧性研究[J]. 中国安全科学学报, 2023, 33(12): 198-205. doi: 10.16265/j.cnki.issn1003-3033.2023.12.2542
	HUANG Xin, TAN Chengsong, WU Kun, et al. Study on resilience of airport infrastructure under blizzard weather[J]. China Safety Science Journal, 2023, 33(12):198-205. doi: 10.16265/j.cnki.issn1003-3033.2023.12.2542
[13]	黄信, 徐平, 吴堃. 降雪天气对机场基础设施系统韧性恢复的影响[J]. 中国安全科学学报, 2024, 34(4):175-182. doi: 10.16265/j.cnki.issn1003-3033.2024.04.1526
	HUANG Xin, XU Ping, WU Kun. Influence of snowfall weather on resilience recovery of airport infrastructure system[J]. China Safety Science Journal, 2024, 34(4): 175-182. doi: 10.16265/j.cnki.issn1003-3033.2024.04.1526
[14]	YAGER R R. OWA aggregation over a continuous interval argument with applications to decision making[J]. IEEE Transactions on Systems, Man and Cybernetics-Part B, 2004, 34(5):1952-1963. doi: 10.1109/TSMCB.2004.831154
[15]	CHEN Xiaoqiang, MENG Jianjun, XU Ruxun, et al. Research on multi-objective optimization methods of urban rail train automatic driving based on NSGA-II[J]. Electronics, 2024, 13(19): 3971-3971. doi: 10.3390/electronics13193971
[16]	FAROQI H. Multiobjective route finding in a multimode transportation network by NSGA-II[J]. Journal of Engineering and Applied Science, 2024,71:DOI:10.1186/s44147-024-00417-7.
[17]	CCAR-140-R2, 运输机场运行安全管理规定(2022版)[S].
	CCAR-140-R2, Safety management regulations of transport airport operations(2022 edition)[S].
[18]	张东, 张彬, 张瑞, 等. 基于熵权-TOPSIS的多能互补联供系统优化配置研究[J]. 华中科技大学学报:自然科学版, 2022, 50(10):136-142.
	ZHANG Dong, ZHANG Bin, ZHANG Rui, et al. Study of optimal allocation of multi-energy complementary cogeneration system based on entropy weight-TOPSIS[J]. Journal of Huazhong University of Science and Technology:Natural Science Edition, 2022, 50(10):136-142.

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