基于风险约束的低空飞行服务站层级选址优化

doi:10.16265/j.cnki.issn1003-3033.2023.05.2258

摘要/Abstract

摘要：

为保障低空通航飞行服务工作的科学运行,引入服务响应与经济成本的风险约束机制和改进多目标遗传算法(MOGA),研究低空飞行服务站选址优化问题。通过分析低空飞行服务保障体系,确定本场区域和转场通道2类飞行需求,提出独立覆盖、联合覆盖和重复覆盖3种服务方式的覆盖函数;根据低空飞行服务站选址的软硬件条件限制,构建风险约束的层级选址决策机制,得到评价指标;运用覆盖决策理论,建立安全风险与服务成本相互约束的多目标优化数学模型;基于Pareto排序设计适应度转换函数,构建“极小-极大”评价方式的MOGA;以四川盆地25万km²低空飞行服务站选址为例,分别运用改进的MOGA和传统的动态规划方法进行求解。结果表明:以覆盖决策为基础的改进MOGA的服务效率提高14.5%、重复覆盖率降低21.3%、安全风险降低13.4%和总成本减少13.2%。基于风险约束的低空飞行服务站选址层级机制有助于均衡低空飞行服务站点布局的安全风险、服务效率与选址成本。

关键词: 风险约束, 低空飞行服务站, 选址优化, 覆盖决策, 多目标遗传算法(MOGA)

Abstract:

In order to ensure the scientific operation of low-altitude general navigation flight service work, the risk constraint mechanism of service response and economic cost was introduced and MOGA was improved to study the location optimization of low-altitude flight service station. Based on analysis of low-altitude flight service support system, the functions of three service coverage modes for two types of demand were determined. According to the hardware and software constraints of low-altitude flight service station location, a risk-constrained hierarchical location decision mechanism and evaluation index were established. Based on coverage decision theory, a multi-objective optimization model of safety risk and service cost was established. Based on Pareto ranking, fitness conversion function was designed, and a multi-objective genetic algorithm with minus-max evaluation method was constructed. It was verified by using 250 000 km² of Sichuan Basin. Compared with dynamic programming method, the results show that the multi-objective improved genetic method based on coverage decision can increase service efficiency by 14.5%, reduce duplicate coverage by 21.3%, reduce security risk by 13.4% and reduce total cost by 13.2%. The hierarchical mechanism of low-altitude flight service station location based on risk constraints balances the safety risk, service efficiency and location cost of low-altitude flight service station layout.

Key words: risk constraints, low-altitude flight service station, location optimization, coverage decision, multi-objective genetic algorithm (MOGA)

陈华群, 杨伟超. 基于风险约束的低空飞行服务站层级选址优化[J]. 中国安全科学学报, 2023, 33(5): 57-65.

CHEN Huaqun, YANG Weichao. Hierarchy location optimization of low-altitude flight service station based on risk constraints[J]. China Safety Science Journal, 2023, 33(5): 57-65.

图/表 15

图1

图2

表1

约束限制与评价目标

约束	服务要素	评价目标	权重占比
空域结构	管制服务/交通情报	E类空域	$ρ i U$ $(U = 1,2, 3)$
	管制/交通咨询/情报	F类空域
	飞行情报	G类空域
飞行种类	农/林/气象/测/油	作业类	$ρ i Q$ $(Q = 1,2, 3,4)$
	飞行培训/体育训练	飞行训练
	公务/调机/检验	其他公务
	专机/应急救援	重要飞行
决策准则	军/民/社会活动	安全性	$ρ j V$ $(V = 1,2, 3)$
	中心/服务站/用户	经济性
	供需弹性/不确定性	鲁棒性
优化目标	服务响应/协助应急	响应时间	$ρ j F$ $(F = 1,2, 3,4)$
	建设/运营/维护成本	服务成本
	信号中断/用户干扰	期望风险
	服务覆盖面积	服务覆盖

表1

图4

图5

图6

表2

表3

Cost of candidate sites 万元

候选S_j	$C j 1$	$C j 2$	$C j 3$	$C j 4$	首年费用
S₁	436.5	299.5	188.1	83.2	1 070.8
S₂	402.8	270.3	169.8	74.3	950.9
S₃	320.3	249.4	156.7	68.6	877.5
S₄	398.9	220.8	138.7	58.9	738.6
S₅	316.2	247.0	155.2	67.9	869.0
S₆	329.5	209.3	131.5	56.5	713.5
S₇	385.7	230.4	144.8	61.2	765.9
S₈	392.1	242.7	152.5	66.4	847.3
S₉	313.6	243.6	153.0	66.9	855.6
S₁₀	306.3	206.8	129.9	55.8	706.2
S₁₁	357.8	203.5	127.9	54.8	692.5
S₁₂	482.2	221.5	139.2	60.9	779.5
S₁₃	383.5	291.2	183.0	80.7	1 037.0
S₁₄	343.9	249.0	156.4	67.5	856.4
S₁₅	317.2	223.9	140.7	60.7	769.2
S₁₆	336.5	214.2	134.6	57.5	723.4
S₁₇	312.6	218.5	137.3	59.3	751.5
S₁₈	300	204.4	128.4	55.3	700.8
S₁₉	324.6	200.5	126.0	53.9	680.4
S₂₀	318.2	217.1	136.4	58.4	736.5
S₂₁	375.9	216.4	135.9	57.9	728.5
S₂₂	436.5	234.2	147.1	64.3	821.5

表3

表4

参数数据取值

参数	取值	参数	取值
M	31	$ρ i u$	0.1/0.42/0.48
N	22	$ρ i q$	0.07/0.89/0.03/0.01
β	2 380万	$ρ j v$	0.75/0.15/0.10
$β'$	800万	$ρ j f$	0.2/0.5/0.15/0.15
γ	11 s	φ	30 %
γ'	6 s	σ	0~0.5
r₁	200 km	r₂	150 km
r₃	100 km	ρ_d	3.33 %

表4

图9

图10

图11

图12

图13

表5

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需求	本场区域/架次	通道1/ 架次	通道2/ 架次	通道3/ 架次	ρ₂/ %	D
w₂	9946	22	220	125	3.6	1/2
w₄	8911	20	192	107	3.5	1/2
w₆	8691	29	142	114	3.2	1/2
w₂₁	6705	15	122	103	3.0	1/2
w₁₀	4283	1	82	51	3.4	1/2
w₂₀	8996	20	132	154	3.0	1/2
w₁₂	8136	18	127	102	3.4	1/2
w₁₈	9042	19	140	116	2.9	1/2
w₁₄	6886	6	165	100	3.3	1/2
w₂₄	9389	23	153	169	3.5	1/2
w₂₂	10726	21	220	131	3.1	1/2
w₃₃	9675	10	153	143	3.3	1/2
w₂₇	3202	1	76	34	3.6	1/2
w₁₇	6708	11	151	86	3.8	1/2
w₃₇	2011	5	48	24	3.7	1/2

序号	需求分配(w_i,s_j)	动态规划	改进MOGA
序号	风险R(s_j,x_i)	动态规划	改进MOGA
1	G₀=(Ω⁰,S⁰)	w₉,w₁₀,w₁₁,w₁₂,w₁₈,w₂₀
1	R⁰	0.64	0.30
2	S₃	w₁,w₂	w₁,w₃
2	R³	0.48	0.27
3	S₁₆	w₃₁,w₂₂	w₂₂,w_29,w₃₁
3	R¹⁶	0.47	0.20
4	S₁₄	w₂₅,w₂₇
4	R³	0.49	0.11
5	S₁₇	w₃₃,w₃₅
5	R¹⁷	0.45	0.10
6	S₁₁	w₁₈,w₃₃	w₁₉,w₃₃
6	R¹¹	0.44	0.09
7	S₂₀	w₂₄,w₃₇
7	R²⁰	0.42	0.14
8	S₉	w₁₃,w₁₄
8	R⁹	0.53	0.24
9	S₂₁	无	w₇,w_6,w_21,w₈
9	R²¹	无	0.21
10	S₁₀	无	w₁₅,w_16,w₁₇
10	R¹⁰	无	0.08
11	T(N)/s	58	36
12	Z₁/s	108.74	105.04
13	Z₂/万元	12 815.8	11 125.4
14	Z₃/km²	525 327	482 927
15	Z₄/%	31.9	18.5
16	ε	1.25	1.09