An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies

Shaoren Wang, Yenchun Jim Wu*, Ruiting Li

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

The demand for emergency medical facilities (EMFs) has witnessed an explosive growth recently due to the COVID-19 pandemic and the rapid spread of the virus. To expedite the location of EMFs and the allocation of patients to these facilities at times of disaster, a location-allocation problem (LAP) model that can help EMFs cope with major public health emergencies was proposed in this study. Given the influence of the number of COVID-19-infected persons on the demand for EMFs, a grey forecasting model was also utilized to predict the accumulative COVID-19 cases during the pandemic and to calculate the demand for EMFs. A serial-number-coded genetic algorithm (SNCGA) was proposed, and dynamic variation was used to accelerate the convergence. This algorithm was programmed using MATLAB, and the emergency medical facility LAP (EMFLAP) model was solved using the simple (standard) genetic algorithm (SGA) and SNCGA. Results show that the EMFLAP plan based on SNCGA consumes 8.34% less time than that based on SGA, and the calculation time of SNCGA is 20.25% shorter than that of SGA. Therefore, SNCGA is proven convenient for processing the model constraint conditions, for naturally describing the available solutions to a problem, for improving the complexity of algorithms, and for reducing the total time consumed by EMFLAP plans. The proposed method can guide emergency management personnel in designing an EMFLAP decision scheme.

原文英語
文章編號9752
期刊International journal of environmental research and public health
19
發行號15
DOIs
出版狀態已發佈 - 2022 8月

ASJC Scopus subject areas

  • 污染
  • 公共衛生、環境和職業健康
  • 健康、毒理學和誘變

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