流行病期间的人员调度和检测策略--以COVID-19为例
Personnel Scheduling and Testing Strategy during Pandemics: The case of COVID-19
摘要：
有效的人员调度在匹配组织中的工作负载需求方面起着重要作用。然而，员工安排有时会受到突发事件（例如COVID-19疫情）影响，导致常规营运中断。由于COVID-19等传染病主要通过与个人的密切接触传播，因此防止传播的有效方法是限制工作场所的现场员工人数并定期进行检测。因此，在疾病暴发期间，确定一个满足组织目标并防止病毒传播的最佳时间安排和检测策略至关重要。在本文中，我们制定这些挑战的框架下，两个混合的非线性规划（MINLP）模型。第一个模型的目的是得出最佳的工作人员占用率和测试策略，以尽量减少员工之间的感染风险，而第二个模型的目的是在随机测试策略下的最佳工作人员占用率。为了解决模型中所表达的问题，我们提出了一个典型的遗传算法以及两个商业求解器。使用真实的和合成的员工接触网络，我们的结果表明，在不同的情况下，遵循推荐的占用和测试策略可以降低25%-60%的感染风险。
英文摘要：
Efficient personnel scheduling plays a significant role in matching workload demand in organizations. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Since infectious diseases like COVID-19 transmit mainly through close contact with individuals, an efficient way to prevent the spread is by limiting the number of on-site employees in the workplace along with regular testing. Thus, determining an optimal scheduling and testing strategy that meets the organization's goals and prevents the spread of the virus is crucial during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second model aims at only optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25\%--60\% under different scenarios.
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