Patient admission scheduling problems with uncertain length of stay: optimization models and an efficient matheuristic approach

In this paper, we study two well-known problems that are attracting increasing attention in the last years: patient admission and patient-to-room assignment problems. These two problems are often complicated by some patients, which may have fluctuations in length of stay (LOS). We propose an optimization model that plans patient admissions and patient stays considering fluctuations in LOS and does not allow overcrowded rooms, as typically required in real-world cases. We develop an efficient matheuristic approach based on large neighborhood search and fix and optimize heuristic. We tested our optimization model and some variations of the objective function on benchmark instances from the literature. The computational results indicate the effectiveness of the proposed methods in improving the quality of care offered to the patients. Furthermore, the proposed approach can provide a useful support to decision makers for patient flow management and avoid disruptions in access to care due to bed shortages.