Dynamic adjustment method for optimizing epidemic-logistics network based on data-driven

LIU Ming, CAO Jie, ZHANG Ding

Systems Engineering - Theory & Practice ›› 2020, Vol. 40 ›› Issue (2) : 437-448.

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Systems Engineering - Theory & Practice ›› 2020, Vol. 40 ›› Issue (2) : 437-448. DOI: 10.12011/1000-6788-2018-1690-12

Dynamic adjustment method for optimizing epidemic-logistics network based on data-driven

  • LIU Ming1, CAO Jie2, ZHANG Ding3
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Abstract

To improve the effect of emergency response in epidemic controlling, the corresponding logistics network should be adjusted dynamically because an unexpected epidemic outbreak has several typical unstructured features, including the fuzzy boundary and time-varying decision scenarios. In this paper, an innovative decision framework for optimizing the epidemic-logistics network based on data-driven is proposed. The whole emergency response time is divided to be multiple and continuous cycles. Emergency response process in each decision-making cycle involves four steps, which are epidemic dynamics analysis, emergency distribution network design, data collection, and parameters adjustment. Under this new decision framework, the entire emergency response process can be converted to an interactive evolution process of data learning and resource optimization. Numerical tests demonstrate that the proposed new decision framework can provide several real-time and effective policies for controlling an unexpected epidemic outbreak. Moreover, it also provides useful decision-making reference for other emergencies.

Key words

unexpected epidemic outbreak / emergency logistics / data-driven / dynamic adjustment / interactive evolution

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LIU Ming , CAO Jie , ZHANG Ding. Dynamic adjustment method for optimizing epidemic-logistics network based on data-driven. Systems Engineering - Theory & Practice, 2020, 40(2): 437-448 https://doi.org/10.12011/1000-6788-2018-1690-12

References

[1] World Bank Group. 2014-2015 West Africa Ebola crisis:Impact update[EB/OL].[2017-9-24]. http://www.worldbank.org/en/topic/macroeconomics/publication/2014-2015-west-africa-ebola-crisis-impact-update.
[2] 闪淳昌, 周玲. 从SARS到大雪灾:中国应急管理体系建设的发展脉络及经验反思[J]. 甘肃社会科学, 2008(5):40-44.Shan C C, Zhou L. From SARS to snowstorm:Development and experience of China's emergency management system construction[J]. Gansu Social Sciences, 2008(5):40-44.
[3] 朱建明. 损毁情景下应急设施选址的多目标决策方法[J]. 系统工程理论与实践, 2015, 35(3):720-727.Zhu J M. Mehtods of multi-objective decision-making for emergency facility location problem under failure scenario[J]. Systems Engineering-Theory & Practice, 2015, 35(3):720-727.
[4] 张辉, 刘奕. 基于“情景-应对”的国家应急平台体系基础科学问题与集成平台[J]. 系统工程理论与实践, 2012, 32(5):947-953.Zhang H, Liu Y. Key problems on fundamental science and technology integration in “scenario-response” based national emergency response platform system[J]. Systems Engineering-Theory & Practice, 2012, 32(5):947-953.
[5] Samsuzzoha M, Singh M, Lucy D. A numerical study on an influenza epidemic model with vaccination and diffusion[J]. Applied Mathematics and Computation, 2012, 219(1):122-141.
[6] Ma Z E, Zhou Y C, Wu J H. Modeling and dynamics of infectious diseases[M]. Beijing:Higher Education Press, 2009.
[7] 金如锋,邱宏,周霞, 等. ARIMA模型和GM(1,1)模型预测全国3种肠道传染病发病率[J]. 复旦学报(医学版), 2008, 35(5):675-680.Jin R F, Qiu H, Zhou X, et al. Forecasting incidence of intestinal infectious diseases in mainland China with ARIMA model and GM(1,1) model[J]. Fudan University Journal (Meidcal Science), 2008, 35(5):675-680.
[8] Lou L X, Chen Y, Yu C H, et al. National HIV/AIDS mortality, prevalence, and incidence rates are associated with the human development index[J]. American Journal of Infection Control, 2014, 42(10):1044-1048.
[9] 刘德海, 王维国, 孙康. 基于演化博弈的重大突发公共卫生事件情景预测模型与防控措施[J]. 系统工程理论与实践, 2012, 32(5):937-946.Liu D H, Wang W G, Sun K. Scenario forecasting model and prevention-control measurements of important public health event based evolutionary game[J]. Systems Engineering-Theory & Practice, 2012, 32(5):937-946.
[10] Ekici A, Keskinocak P, Swann J L. Modeling influenza pandemic and planning food distribution[J]. Manufacturing & Service Operations Management, 2014, 16(1):11-27.
[11] 朱莉,曹杰. 面向灾害扩散的模糊需求下应急调配优化研究[J]. 系统科学与数学, 2014, 34(6):663-673.Zhu L, Cao J. Emergency resource allocation optimization under disaster spreading with fuzzy demand[J]. Journal of Systems Science and Mathematical Sciences, 2014, 34(6):663-673.
[12] He Y X, Liu N. Methodology of emergency medical logistics for public health emergencies[J]. Transportation Research Part E:Logistics and Transportation Review, 2015, 79:178-200.
[13] Liu M, Zhang D. A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating[J]. Journal of the Operational Research Society, 2016, 67(6):841-852.
[14] Dasaklis T K, Rachaniotis N P, Pappis C P. Emergency supply chain management for controlling a smallpox outbreak:The case for regional mass vaccination[J]. International Journal of Systems Science:Operations & Logistics, 2017, 4(1):27-40.
[15] Dasaklis T K, Pappis C P, Rachaniotis N P. Epidemics control and logistics operations:A review[J]. International Journal of Production Economics, 2012, 139(2):393-410.
[16] Teytelman A, Larson R C. Multiregional dynamic vaccine allocation during an influenza epidemic[J]. Service Science, 2013, 5(3):197-215.
[17] Juusola J L, Brandeau M L. HIV treatment and prevention:A simple model to determine optimal investment[J]. Medical Decision Making:An International Journal of the Society for Medical Decision Making, 2015, 36(3):391.
[18] Liu M, Zhang Z, Zhang D. A dynamic allocation model for medical resources in the control of influenza diffusion[J]. Journal of Systems Science and Systems Engineering, 2015, 24(3):276-292.
[19] Chen W Y, Alain G, Angel R. Modeling the logistics response to a bioterrorist anthrax attack[J]. European Journal of Operational Research, 2016, 254(2):458-471.
[20] Buyuktahtakin I E, Des-Bordes E, Kibis E Y. A new epidemics-logistics model:Insights into controlling the Ebola virus disease in West Africa[J]. European Journal of Operational Research, 2018, 265(3):1046-1063.
[21] Brandeau M L. Creating impact with operations research in health:Making room for practice in academia[J]. Health Care Management Science, 2016, 19(4):305-312.
[22] Tan X, Yuan L, Zhou J, et al. Modeling the initial transmission dynamics of influenza a H1N1 in Guangdong province, China[J]. International Journal of Infectious Diseases, 2013, 17(7):479-484.
[23] Tuite A R, Greer A L, Whelan M, et al. Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza[J]. Canadian Medical Association Journal, 2010, 182(2):131-136.

Funding

National Natural Science Foundation of China (71771120); National Social Science Foundation of China (16ZDA054); Humanities and Social Sciences Foundation of Ministry of Education of China (17YJA630058); Six Major Talents Peak Project of Jiangsu Province (XYDXXJS-CXTD-005)
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