In a home care setting, high-quality care is typically associated with continuity of care. In addition, the increasing pressure due to labor shortages calls for cost-efficient operations. This paper focuses on obtaining cost-efficient daily schedules over a longer time horizon, with balanced shift lengths, while ensuring continuity of care (using the continuity of care index). To address this challenge, we propose a novel method based on blueprint routes. This method generates daily schedules by constructing optimized shifts and routes with regard to travel time, (time window) waiting time, and shift costs based on hourly wages. To ensure continuity of care, the daily scheduling decisions are strategically guided using the concept named blueprint routes. The blueprint routes are pre-optimized (partial) routes that help to align the daily schedules to achieve continuity of care in the subsequent nurse-to-shift assignment. Model-based evolutionary algorithms are employed to overcome the NP-hardness of the routing problem and nurse-to-shift assignment. Real-life-based numerical experiments demonstrate that continuity of care does not have to compromise home care schedule costs significantly.

In many countries, the rapid aging of the population leads to an additional burden on already stretched long-term care systems. This often manifests itself in excessive waiting times for long-term care centers, and in abandonments (i.e., patients passing away while they are waiting). Interestingly, in practice, long waiting times are not caused by a lack of available total capacity in the system, but by systematic inefficiencies in the allocation of patients, each with their personal preferences and (in)flexibility, to geographically distributed care centers.Motivated by this, we propose a new and easy-to-implement method for the optimal allocation of patients-in-need to nursing homes, balancing the trade-off between the waiting time performance and the individual patients’ preferences and levels of flexibility. The optimal placement policy found by solving a Markov Decision Process demonstrates that for small instances, the mean optimality gap of the allocation model is equal to 1. 3%. We validate a simulation model for a real-life use case of allocating somatic patients to nursing homes in the Amsterdam area. The results show that if more patient replacements are approved, the allocation model can reduce the abandonment fraction under the current policy from 32.2% to 7.4% and waiting times at the same time. Moreover, with the allocation model individual preferences can be served better, which thus provides a powerful means to face the increasing need for patient-centered and sustainable long-term care solutions.

We develop a modeling framework for rostering, absence and demand uncertainty realization, and rerostering to perform detailed quantitative analyses of the robustness of nurse rosters. The framework reflects a real-life problem observed at the Department of Neonatal Intensive Care (DNIC) at St. Olavs Hospital in Trondheim, Norway, but is general and has a high transfer value with respect to using it to analyze roster robustness at other departments. We present multiple proactive strategies to enhance the stability of a roster and a reactive rerostering problem used to improve the flexibility. An extensive case study is performed using historical data from the department. The results show that there is a great potential to improve the stability and flexibility of the rosters using the best combination of strategies. We show that allowing nurses to trade extra weekend work for extra days off, assign surplus work hours evenly over all work shifts, and consider the absence profile of nurses when making the rosters are key strategies to create robust rosters.