A fuzzy goal programming approach for synchronizing economic and social measures in a decentralized home palliative care network

Home palliative care enhances patients' quality of life by delivering compassionate care at home, reducing hospital visits and overall healthcare costs. This study proposes a novel bi-level programming model to optimize healthcare networks, specifically for home palliative care services. The upper level maximizes social objectives such as accessibility and equity, while the lower level minimizes economic costs. A new accessibility function is introduced, incorporating travel time, traffic distance, and patient age. Additionally, demand is estimated using patient characteristics and workload-based staffing indicators. To address the complexities of the hierarchical structure and uncertainty in parameters, the model uses a Fuzzy Goal Programming (FGP) approach integrated with Fuzzy Chance-Constrained Programming (FCCP). The model is validated through a real case study at the Iranian Cancer Control Center. This research contributes to healthcare logistics by integrating home palliative care into decentralized planning, effectively balancing social and economic priorities.