This dissertation tackles the growing challenges in home healthcare, such as increasing patient demand, staff shortages, and budget constraints. It focuses on improving scheduling efficiency while considering care worker satisfaction, which is crucial for retaining staff. The research identifies limitations in existing models, which often optimize short-term decisions sequentially and overlook key characteristics of home healthcare.nnTo address these gaps, the thesis introduces a matheuristic algorithm that integrates rostering, assignment, routing, and scheduling over a four-week period. This approach accounts for real-life constraints and significantly improves operational efficiency compared to traditional methods. It shows that patient flexibility and limited overtime can enhance scheduling outcomes, and that continuity of care can be maintained without sacrificing efficiency when decisions are integrated.nnThe second part of the thesis explores weekly re-planning after demand changes, using a bi-objective optimization model to balance cost and care worker satisfaction. A heuristic algorithm is developed to manage this complex problem efficiently. Results reveal that reducing roster deviations is cost-effective up to a point, and that limited patient flexibility increases both costs and scheduling disruptions.nnOverall, the dissertation offers innovative, integrated decision-making strategies that outperform sequential approaches and provides valuable insights for future research in home healthcare planning.