A Clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations

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

Abstract

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

CLC Number:

TP391.9

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

Figures/Tables 11

Fig. 1

Table 1

Notations used in this work

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

Table 1

Fig. 2

Table 2

Fig. 3

Fig. 4

Fig. 5

Table 3

Table 4

Table 5

Fig. 6

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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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