Eriskin Levent, Karatas Mumtaz, Zheng Yu-Jun
Department of Industrial Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey.
School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, 311121 Zhejiang China.
Ann Oper Res. 2022 May 21:1-48. doi: 10.1007/s10479-022-04760-x.
In this study, we consider the problem of healthcare resource management and location planning problem during the early stages of a pandemic/epidemic under demand uncertainty. Our main ambition is to improve the preparedness level and response effectiveness of healthcare authorities in fighting pandemics/epidemics by implementing analytical techniques. Building on lessons from the Chinese experience in the COVID-19 outbreak, we first develop a deterministic multi-objective mixed integer linear program (MILP) which determines the location and size of new pandemic hospitals (strategic level planning), periodic regional health resource re-allocations (tactical level planning) and daily patient-hospital assignments (operational level planning). Taking the forecasted number of cases along a planning horizon as an input, the model minimizes the weighted sum of the number of rejected patients, total travel distance, and installation cost of hospitals subject to real-world constraints and organizational rules. Next, accounting for the uncertainty in the spread speed of the disease, we employ an across scenario robust (ASR) model and reformulate the robust counterpart of the deterministic MILP. The ASR attains relatively more realistic solutions by considering multiple scenarios simultaneously while ensuring a predefined threshold of relative regret for the individual scenarios. Finally, we demonstrate the performance of proposed models on the case of Wuhan, China. Taking the 51 days worth of confirmed COVID-19 case data as an input, we solve both deterministic and robust models and discuss the impact of all three level decisions to the quality and performance of healthcare services during the pandemic. Our case study results show that although it is a challenging task to make strategic level decisions based on uncertain forecasted data, an immediate action can considerably improve the response effectiveness of healthcare authorities. Another important observation is that, the installation times of pandemic hospitals have significant impact on the system performance in fighting with the shortage of beds and facilities.
在本研究中,我们考虑了在大流行/疫情早期阶段,需求不确定情况下的医疗资源管理和选址规划问题。我们的主要目标是通过实施分析技术,提高医疗当局应对大流行/疫情的准备水平和响应效率。基于中国在新冠疫情爆发中的经验教训,我们首先开发了一个确定性多目标混合整数线性规划模型(MILP),该模型用于确定新建大流行医院的位置和规模(战略层面规划)、定期区域卫生资源重新分配(战术层面规划)以及每日患者与医院的分配(运营层面规划)。以规划期内预测的病例数作为输入,该模型在满足现实约束和组织规则的前提下,最小化被拒患者数量、总出行距离和医院建设成本的加权总和。接下来,考虑到疾病传播速度的不确定性,我们采用跨情景鲁棒(ASR)模型,并重新制定确定性MILP的鲁棒对偶模型。ASR通过同时考虑多个情景,在确保单个情景相对遗憾的预定义阈值的同时,获得相对更现实的解决方案。最后,我们在中国武汉的案例中展示了所提出模型的性能。以51天的新冠确诊病例数据作为输入,我们求解了确定性模型和鲁棒模型,并讨论了所有三个层面决策对疫情期间医疗服务质量和性能的影响。我们的案例研究结果表明,尽管基于不确定的预测数据做出战略层面决策是一项具有挑战性的任务,但立即采取行动可以显著提高医疗当局的响应效率。另一个重要发现是,大流行医院的建设时间对应对床位和设施短缺时的系统性能有重大影响。