Govindan Kannan, Mina Hassan, Alavi Behrouz
China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China.
Centre for Sustainable Supply Chain Engineering, Department of Technology and Innovation, Danish Institute for Advanced Study, University of Southern Denmark, Odense M 5230, Denmark.
Transp Res E Logist Transp Rev. 2020 Jun;138:101967. doi: 10.1016/j.tre.2020.101967. Epub 2020 May 7.
The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.
其长期的破坏以及不断加剧的传播。不控制此类灾难会给供应链和社区带来严重破坏，进而会造成无法挽回的损失。2019冠状病毒病（COVID-19）就是这些灾难之一，它在全球范围内以及许多供应链中，尤其是医疗供应链中造成了严重破坏。因此，本文首次基于医生的知识和模糊推理系统（FIS）开发了一个实用的决策支持系统，以帮助医疗供应链中的需求管理，减轻社区压力，打破COVID-19传播链，并总体上缓解医疗供应链中断导致的疫情爆发。该方法首先根据社区居民免疫系统的风险水平（即非常敏感、敏感、轻度敏感和正常）以及年龄和既有疾病（如糖尿病、心脏病或高血压）这两个指标将居民分为四组。然后，对这些个体进行分类，并要求他们遵守所属类别的规定。最后，利用来自四个用户的信息在现实世界中对所提方法的效率进行了衡量，结果表明了所提方法的有效性和准确性。
