基于聚类的疫情物资投放点选址方法研究

doi:10.16182/j.issn1004731x.joss.24-FZ0740E

摘要/Abstract

摘要：

为解决常用的智能优化算法在求解选址问题时，在有效性、效率和稳定性方面表现不佳的问题，提出了一种新的在隔离期间物资投放点的选择方法。在确定优化目标和约束条件后，根据所收集的数据建立了相关数学模型，并采用传统的智能优化算法得到Pareto前沿解；基于这些Pareto前沿解的特点，提出了一种改进版聚类算法，并利用长春市的相关数据进行了仿真实验。结果表明：所提算法在有效性、效率和稳定性方面均优于传统的智能优化算法，与基准算法相比在时间成本和人力成本上分别降低了约12%和8%。

关键词: 选址问题, 聚类算法, 智能优化算法, Pareto 前沿

Abstract:

To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness, efficiency, and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic quarantines. After establishing the optimization objectives and constraints, we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal solutions. Building on the characteristics of these Pareto front solutions, we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun City. The results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness, efficiency, and stability, achieving reductions of approximately 12% and 8% in time and labor costs, respectively, compared to the baseline algorithm.

Key words: location problem, clustering algorithm, intelligent optimization algorithm, Pareto front

中图分类号:

TP391.9

周雅琼,陈俊琪,李惟时等 . 基于聚类的疫情物资投放点选址方法研究[J]. 系统仿真学报, 2024, 36(12): 2782-2796.

Zhou Yaqiong,Chen Junqi,Li Weishi,et al . A Clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations[J]. Journal of System Simulation, 2024, 36(12): 2782-2796.

图/表 11

Notations	Descriptions
$C$	Set of communities, $i$ , $j$ $∈$ $C$
$P i$	The population of the community $i$
$x i$	The abscissa of community
$y i$	The ordinate of community
$D i j$	Distance between community $i$ and community $j$
$n$	The number of communities
$z$	Total labor costs
$t$	Time costs
$v$	The delivery speed is 20 km/h
$m$	The number of delivery sites
$O j$	$O j$ is 1, if the community $j$ is selected as a delivery site; 0 otherwise
$s i j$	$s i j$ is 1, if the community $i$ is served by a delivery site $j$ ; 0 otherwise

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Serial number	Total labor cost (p∙km)	Time cost/min	Solution
I	790 618.477	11.91	[182, 230, 256, 284, 321, 234, 336, 242, 233]
II	655 154.207	13.29	[230, 229, 215, 276, 247, 174, 199, 228, 322]
III	653 759.240	15.39	[310, 248, 207, 177, 206, 313, 265, 249, 173]
IV	653 533.170	15.69	[304, 183, 191, 310, 235, 311, 172, 336, 204]

Parameter	Value
Number of particles	100
Number of iterations	100
Individual learning factor	2
Group learning factor	2
Inertia weight	0.7

	Total labor costs (person·km)			Time costs/min
	GA	PSO	KMW	GA	PSO	KMW
Mean value	684 484.44	701 173.23	629 973.15	15.39	15.69	13.62
Standard deviation	123 736.04	74 437.52	43 641.80	8.31	9.54	3.51

Effectiveness		P-value
Comparison between KMW and GA	Total labor cost	3×10^-11
Comparison between KMW and GA	Time cost	5.63×10^-7
Significance test for improvement	Total labor cost	0.31
Significance test for improvement	Time cost	0.81

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