Journal of System Simulation
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.
Recommended Citation
Zhou, Yaqiong; Chen, Junqi; Li, Weishi; Qiu, Sihang; and Ju, Rusheng
(2024)
"A Clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations,"
Journal of System Simulation: Vol. 36:
Iss.
12, Article 4.
DOI: 10.16182/j.issn1004731x.joss.24-FZ0740E
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss12/4
First Page
2782
Last Page
2796
CLC
TP391.9
Recommended Citation
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, 20274, 36(12): 2782-2796.
DOI
10.16182/j.issn1004731x.joss.24-FZ0740E
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