Moosavi Amirhossein, Ozturk Onur, Patrick Jonathan
University of Ottawa, Telfer School of Management, 55 Laurier Avenue East, Ottawa, Ontario K1N 6N5, Canada.
Omega. 2022 Oct;112:102671. doi: 10.1016/j.omega.2022.102671. Epub 2022 May 4.
The COVID-19 pandemic severely impacted residential care delivery all around the world. This study investigates the current scheduling methods in residential care facilities in order to enhance them for pandemic conditions. We first define the basic problem that addresses decisions associated with the assignment and scheduling of staff members, who perform a set of tasks required by residents during a planning horizon. This problem includes the minimization of costs associated with the salary of part-time staff members, total overtime, and violations of service time windows. Subsequently, we adapt the basic problem to pandemic conditions by considering the impacts of communal spaces (e.g., shared rooms) and a cohorting policy (classification of residents based on their risk of infection) on the spread of infectious diseases. We introduce a new objective function that minimizes the number of distinct staff members serving each room of residents. Likewise, we propose a new objective function for the cohorting policy that aims to minimize the number of distinct cohorts served by each staff member. A new constraint is incorporated that forces staff members to serve only one cohort within a shift. We present a population-based heuristic algorithm to solve this problem. Through a comparison with two benchmark solution approaches (a mathematical programme and a non-dominated archiving ant colony optimization algorithm), the superiority of the heuristic algorithm is shown regarding solution quality and CPU time. Finally, we conduct numerical analyses to present managerial implications.
新冠疫情对全球的住宿式护理服务产生了严重影响。本研究调查了住宿式护理机构当前的排班方法，以便针对疫情情况对其进行改进。我们首先定义了一个基本问题，该问题涉及与工作人员的分配和排班相关的决策，这些工作人员在一个规划期内执行居民所需的一系列任务。这个问题包括将与兼职工作人员工资、总加班时间以及服务时间窗口违规相关的成本降至最低。随后，我们通过考虑公共空间（如共享房间）和群组政策（根据居民的感染风险对居民进行分类）对传染病传播的影响，使基本问题适应疫情情况。我们引入了一个新的目标函数，该函数使服务每个居民房间的不同工作人员数量最小化。同样，我们为群组政策提出了一个新的目标函数，旨在使每个工作人员服务的不同群组数量最小化。引入了一个新的约束条件，强制工作人员在一个班次内仅服务一个群组。我们提出了一种基于种群的启发式算法来解决这个问题。通过与两种基准求解方法（一种数学规划方法和一种非支配存档蚁群优化算法）进行比较，在解的质量和CPU时间方面显示了启发式算法的优越性。最后，我们进行数值分析以呈现管理启示。
