This study presents an efficient solution for the integrated recovery room planning and scheduling problem (IRRPSP). The complexity of the IRRPSP is caused by several sources. The problem combines the assignment of patients to recovery rooms and the scheduling of caregivers over a short-term planning horizon. Moreover, a solution of the IRRPSP should respect a set of hard and soft constraints while solving the main problem such as the maximum capacity of recovery rooms, the maximum daily load of caregivers, the treatment deadlines, etc. Thus, the need for an automated tool to support the decision-makers in handling the planning and scheduling tasks arises. In this paper, we present an exhaustive description of the epidemiological situation within the Kingdom of Saudi Arabia, especially in Jeddah Governorate. We will highlight the importance of implementing a formal and systematic approach in dealing with the scheduling of recovery rooms during extreme emergency periods like the COVID-19 era. To do so, we developed a mathematical programming model to present the IRRPSP in a formal way which will help in analyzing the problem and lately use its solution for comparison and evaluation of our proposed approach. Due to the NP-hard nature of the IRRPSP, we propose a hybrid three-level approach. This study uses real data instances received from the Department of Respiratory and Chest Diseases of the King Abdulaziz Hospital. The computational results show that our solution significantly outperforms the results obtained by CPLEX software with more than 1.33% of satisfied patients on B1 benchmark in much lesser computation time (36.27 to 1546.79 s). Moreover, our proposed approach can properly balance the available nurses and the patient perspectives.
Keywords: COVID-19 pandemic; Combinatorial optimization problem; Decision support system; Healthcare management; Patient assignment.
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