考虑灾后模糊需求与道路受损的应急物资配送模型及算法

doi:10.16265/j.cnki.issn1003-3033.2025.01.0374

摘要/Abstract

摘要：

在重大灾害发生初期,在受灾点需求不确定、受灾区域道路损坏以及考虑救援工作的公平性和及时性等影响下,为实现迅速而有效地配送应急物资,应用社交网络搜索算法(SNS)求解应急物资配送模型。首先,在考虑模糊需求和受灾道路损坏的基础背景下,以应急救援总成本和人道主义救援评价为目标,构建应急物资配送模型;然后,在求解模型时引入SNS算法,并提出强化学习率策略的改进SNS(ISNS);最后,以2022年四川泸定地震为例,分别应用SNS算法、ISNS算法、离散粒子群算法、遗传算法和模拟退火算法这5种求解该算例。结果表明: ISNS算法具有稳定性,求解结果与其他算法相比,应急救援总成本至少节省6 410元,人道主义救援评价目标至少提升50.6%,体现出ISNS算法的优越性。ISNS算法有利于应急物资配送问题求解。

关键词: 模糊需求, 道路受损, 应急物资配送, 社交网络搜索算法(SNS), 时间满意度

Abstract:

In the early stages of a major disaster, where demand at disaster sites is uncertain, roads are damaged in affected areas, and the fairness and timeliness of rescue operations must be considered, the SNS algorithm is applied to solve the emergency supply distribution model to achieve rapid and effective distribution of emergency supplies. First, an emergency supply distribution model was constructed, with the objective of minimizing the total cost of emergency rescue and the evaluation of humanitarian aid under the background of fuzzy demand and damaged roads. Then, the SNS algorithm was introduced to solve the model, and an improved SNS(ISNS) algorithm with a reinforcement learning rate strategy was proposed. Finally, taking the 2022 Luding Earthquake in Sichuan as an example, the SNS algorithm, ISNS algorithm, discrete particle swarm optimization, genetic algorithm, and simulated annealing algorithm were applied to solve this case, respectively. The results indicate that the ISNS algorithm demonstrates stability. Compared with other algorithms, the total cost of emergency rescue is reduced by at least 6 410 yuan, and the evaluation of the humanitarian aid evaluation target is improved by at least 50.6%, highlighting the superiority of the ISNS algorithm. The ISNS algorithm is beneficial for solving emergency supply distribution problems.

Key words: fuzzy demand, road damaged, emergency supplies distribution, social network search(SNS), time satisfaction

中图分类号:

X913.1安全运筹学

曾小青, 刘理明, 程泽禹. 考虑灾后模糊需求与道路受损的应急物资配送模型及算法[J]. 中国安全科学学报, 2025, 35(1): 186-193.

ZENG Xiaoqing, LIU Liming, CHENG Zeyu. Emergency supply distribution model and its algorithm considering post-disaster fuzzy demand and road damage[J]. China Safety Science Journal, 2025, 35(1): 186-193.

图/表 12

图1

图2

表1

表2

车辆参数

车辆类型	容量/ kg	平均行驶速度/ (km·h^-1)	单位距离成本/ (元·km^-1)
$A 1$	2 100	50	10.0
$A 2$	650	30	3.1
$A 3$	360	20	1.7

表2

表3

受灾点的时间满意度参数

需求点	等待时间/h	需求点	等待时间/h
泸定县 $I 5$	[1.31,57.61]	石棉县 $I 10$	[0.96,76.96]
汉源县 $I 6$	[3.14,68.34]	雨城区 $I 11$	[1.66,75.17]
芦山县 $I 7$	[2.96,76.96]	天全县 $I 12$	[2.39,50.00]
九龙县 $I 8$	[1.47,86.27]	荥经县 $I 13$	[0.42,71.90]
甘洛县 $I 9$	[0.28,37.55]

表3

表4

受灾点应急物资模糊需求量

需求点	模糊需求量/kg	需求点	模糊需求量/kg
泸定县 $I 5$	[3 200,3 450,3 600]	石棉县 $I 10$	[3 000,3 200,3 400]
汉源县 $I 6$	[3 400,3 600,3 800]	雨城区 $I 11$	[400,580,700]
芦山县 $I 7$	[600,820,1 000]	天全县 $I 12$	[800,1 000,1 200]
九龙县 $I 8$	[200,440,600]	荥经县 $I 13$	[3 000,3 200,3 400]
甘洛县 $I 9$	[1 100,1 350,1 500]

表4

表5

受灾点应急物资需求量

需求点	需求量/kg	需求点	需求量/kg
泸定县 $I 5$	3 430	石棉县 $I 10$	3 200
汉源县 $I 6$	3 600	雨城区 $I 11$	568
芦山县 $I 7$	812	天全县 $I 12$	1 000
九龙县 $I 8$	424	荥经县 $I 13$	3 200
甘洛县 $I 9$	1 330

表5

表6

运输网络距离参数

地点	$I 1$	$I 2$	$I 3$	$I 4$	$I 5$	$I 6$	$I 7$	$I 8$	$I 9$	$I 10$	$I 11$	$I 12$	$I 13$
$I 1$	0	—	123	57	—	—	—	—	—	—	127	—	—
$I 2$	—	0	—	75	—	—	—	—	—	—	—	—	—
$I 3$	123	—	0	—	—	111	—	—	116	—	85	—	—
$I 4$	57	75	—	0	—	—	115	—	—	—	—	—	—
$I 5$	—	—	—	—	0	74	—	—	—	77	—	54	61
$I 6$	—	—	111	—	74	0	—	—	43	30	—	—	53
$I 7$	—	—	—	115	—	—	0	—	—	—	19	19	—
$I 8$	—	—	—	—	—	—	—	0	—	87	—	—	—
$I 9$	—	—	116	—	—	43	—	—	0	50	—	—	—
$I 10$	—	—	—	—	77	30	—	87	50	0	—	—	—
$I 11$	127	—	85	—	—	—	19	—	—	—	0	25	26
$I 12$	—	—	—	—	54	—	19	—	—	—	25	0	31
$I 13$	—	—	—	—	61	53	—	—	—	—	26	31	0

表6

表7

图3

表8

图4

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配送中心	容量/kg	固定建设费用/元
成都	15 000	5 000
绵阳	8 000	4 800
广元	4 000	4 000

应急救援总成本/元	38 670.0
满足程度/%	84.345
满足方差	9.699 832×10^-7
时间满意度	5.672
人道主义救援评价	15.248

算法	应急救援总成本/元	满足程度/%	满足方差	时间满意度	人道主义救援评价	模型适应度	运行时间
ISNS	38 670.0	84.354	9.699 832×10^-7	5.672	15.248	1.186	3 min 47 s
SNS	45 080.0	84.946	8.899 038×10^-6	3.844	30.895	1.985	3 min 48 s
DPSO	57 764.4	82.064	8.367 739×10^-5	4.237	37.420	2.464	4 min 39 s
GA	59 780.0	84.269	3.744 398×10^-4	20.850	52.085	3.035	4 min 52 s
SA	70 455.7	78.177	8.654 783×10^-5	4.662	35.358	2.642	4 min 4 1s