Emergency supply distribution model and its algorithm considering post-disaster fuzzy demand and road damage

doi:10.16265/j.cnki.issn1003-3033.2025.01.0374

Abstract

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

CLC Number:

X913.1安全运筹学

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.

Figures/Tables 12

Fig.1

Fig.2

Table 1

Table 2

Parameters of vehicles

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

Table 2

Table 3

Time satisfaction parameters for disaster sites

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]

Table 3

Table 4

Fuzzy demand for emergency supplies at disaster sites

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]

Table 4

Table 5

Emergency supplies demand at disaster sites

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

Table 5

Table 6

Distance parameters of transportation networks

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

Table 6

Table 7

Fig.3

Table 8

Fig.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