Çok dönemli evde sağlık hizmetleri rotalama ve çizelgeleme problemi için matematiksel sezgisel algoritma: gerçek vaka çalışması

The aging population's exponential growth has exerted considerable pressure on healthcare systems, necessitating the provision of enhanced healthcare services tailored to meet the unique needs of older adults, individuals with disabilities, and chronic patients. As a result, healthcare providers aim to offer varying healthcare services to patients in their homes, with the objective of improving the quality of care and optimizing the management of health systems. This thesis studies a home healthcare routing and scheduling problem (HHCRSP) over a multi-period planning horizon, considering caregivers' lunch breaks, prior service type, and patients' preferences. In this HHCRSP, some patients needing blood draw, and they have to be visited before noon, guaranteeing the corresponding caregiver's return to the hospital's lab before noon. Additionally, patients have preferred time windows for each day, corresponding to times due to reasons such as the need for someone to support them with the patients. In the thesis, the objective function is minimizing the total routing costs of caregivers' vehicles and the penalty costs incurred when patients cannot receive services within their preferred time windows. Our study is motivated by a real-life hospital that provides home healthcare service (HHCS). We develop a mixed-integer linear programming (MILP) model and propose a simulated annealingbased matheuristic algorithm (SAMA) that decomposes the original problem into two phases. Furthermore, we conduct a comparative analysis of the k-nearest-neighbour (KNN) algorithm, utilizing various k values to predict service times. The results of our study demonstrate significant improvements, up to 98.64% in cost-effectiveness achieved by the MILP in small-sized instances, and 98.58% by the SAMA in medium and large-sized instances, compared to the existing system in the motivational hospital. The numerical analysis provides insights for healthcare providers and policymakers in their efforts to optimize HHCS.