Franco Carlos, Herazo-Padilla Nilson, Castañeda Jaime Andrés
School of Management and Business, Universidad del Rosario, Bogotá - Colombia.
Data Science Consultant.
Vaccine. 2022 Nov 22;40(49):7073-7086. doi: 10.1016/j.vaccine.2022.09.079. Epub 2022 Oct 3.
This paper considers the problem of patient scheduling and capacity planning for the vaccination process during the COVID-19 pandemic. The proposed solution is based on a non-linear mathematical modeling approach representing the dynamics of an open Jackson Network and a Generalized Network. To test these models, we proposed three objective functions and analyzed different configurations of the process corresponding to various levels of the models' parameters as well as the conditions present in the case study. To assess the computational performance of the models, we also experimented with larger instances in terms of number of steps or stations used and number of patients scheduled. The computational results show how parameters such as the minimum percentage of patients served, the maximum occupation allowed per station and the objective functions used have an impact on the configuration of the process. The proposed approach can support the decision-making process in vaccination centers to efficiently assign human and material resources to maximize the number of patients vaccinated while ensuring reasonable waiting times, number of patients in queue and servers' utilization rates, which in turn are key to avoid overcrowding and other negative conditions in the system that could increase the risk of infections.
本文探讨了新冠疫情期间疫苗接种过程中的患者调度和容量规划问题。所提出的解决方案基于一种非线性数学建模方法，该方法描述了开放杰克逊网络和广义网络的动态特性。为了测试这些模型，我们提出了三个目标函数，并分析了与模型参数的不同水平以及案例研究中存在的条件相对应的过程的不同配置。为了评估模型的计算性能，我们还针对所使用的步骤数或站点数以及调度的患者数等更大的实例进行了实验。计算结果表明，诸如服务患者的最小百分比、每个站点允许的最大占用率以及所使用的目标函数等参数如何对过程的配置产生影响。所提出的方法可以支持疫苗接种中心的决策过程，以便有效地分配人力和物力资源，在确保合理的等待时间、排队患者数量和服务器利用率的同时，最大化接种疫苗的患者数量——这些因素反过来又是避免系统过度拥挤以及可能增加感染风险的其他负面情况的关键。
