Journal of System Simulation
Abstract
Abstract: In view of the safety risks associated with logistics distribution route optimization during public health emergencies, this paper investigates the vehicle routing problem by incorporating the risk of cross-infection, integrates the cross-infection risk caused by logistics activities in the epidemic area into the logistics distribution model, and establishes a logistics vehicle distribution model with the goal of cross-infection risk and cost. An improved genetic algorithm is designed for model optimization and solution. Based on the integration of chaos initialization population and adaptive crossover and mutation operations, a neighbor exclusion operator is further proposed to enhance the global search ability of the algorithm, prevent premature convergence, and ensure the diversity of chromosome population. Through numerical simulation and experimental comparative analysis, it is verified that the model and optimization algorithm established in this paper are effective and feasible in solving logistics distribution problems under public health events.
Recommended Citation
Shi, Xiaodong; Guo, Yongcheng; Ma, Mingqi; and Pan, Jiarui
(2025)
"Optimization of Vehicle Routing for Cross-infection Risk in the Epidemic,"
Journal of System Simulation: Vol. 37:
Iss.
4, Article 7.
DOI: 10.16182/j.issn1004731x.joss.23-1476
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss4/7
First Page
910
Last Page
921
CLC
TP391.9
Recommended Citation
Shi Xiaodong, Guo Yongcheng, Ma Mingqi, et al. Optimization of Vehicle Routing for Cross-infection Risk in the Epidemic[J]. Journal of System Simulation, 2025, 37(4): 910-921.
DOI
10.16182/j.issn1004731x.joss.23-1476
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