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作 者:刘明[1] 王洁[1] LIU Ming;WANG Jie(School of Economics and Management,Nanjing University of Science and Technology,Nanjing,Jiangsu,210094)
机构地区:[1]南京理工大学经济管理学院,江苏南京210094
出 处:《南京理工大学学报(社会科学版)》2024年第3期1-11,共11页Journal of Nanjing University of Science and Technology:Social Sciences
基 金:国家自科基金资助项目“考虑交互耦合效应的疫情应急物资多元供给模式与优化配置方法研究”(72171119);江苏省研究生科研与实践创新计划项目“重大突发疫情下的应急支援结对关系匹配优化研究”(KYCX23_0612)。
摘 要:重大突发事件中的对口支援是典型的具有中国特色的应急救援措施,也是我国社会制度优越性的重要体现。现有相关研究大多为定性研讨,鲜有从管理科学视角探讨供需双方如何实现匹配优化。研究应急响应阶段中的对口支援关系优化问题,并以全国支援湖北抗击疫情为例:首先从“人”“机”“物”等角度界定支援地供应能力,同时创新定义了受灾地的需求紧迫度和供需匹配函数,然后借鉴指派优化问题的建模思想,以加权的需求满足率最大化为目标函数,构建了应急响应阶段的对口支援结对关系匹配优化模型。计算实验表明:所提出的模型兼顾了应急资源分配的公平性,能最大限度地覆盖灾区需求情况;在资源紧缺和资源充裕两个极端情况下,“物”和“人”分别是提升灾区需求满足率的关键。As a typically Chinese major emergency rescue measure,counterpart support significantly manifests Chinese social system superiority.While most studies in this respect are qualitative,few of them focus on supply and demand matching optimization with regards to management science.Taking the nationwide support for Hubei Province against COVID-19 as a case in point,this paper investigates the counterpart support relationship optimization at the emergency response stage.It first makes a supply capacity definition of the support areas from the perspectives of human resource,machinery and material supplies.Meanwhile,it innovatively constructs the demand urgency and its supply-demand matching function of the disaster-afflicted areas.With a reference to the modeling concept of assignment optimization,this paper proposes the optimization model of paired support relationships at the emergency response stage,with the objective function of maximizing the weighted demand satisfaction rate.Results from experimental calculations illustrate that this proposed model considers the emergency resource allocation fairness,with the maximum coverage of the needs in the disaster-afflicted areas.In terms of two extreme situations of resource shortage and resource abundance,material supplies and human resource are respectively key to the demand satisfaction rate improvement in disaster-afflicted areas.
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