The increasing occurrence of health crises, pandemics, and supply chain disruptions has highlighted the importance of designing resilient and robust healthcare networks. Under such conditions, decision-making regarding healthcare facility location, patient allocation, capacity planning, and medical equipment supply under uncertainty has become a major challenge for healthcare systems. This study proposes a robust and resilient multi-objective mixed-integer linear programming model for healthcare facility location-allocation that simultaneously integrates location, service allocation, capacity planning, and medical equipment supply decisions under demand uncertainty and supply chain disruptions. To model uncertainty, a scenario-based robust optimization approach based on the Mulvey framework is employed, which considers not only the minimization of expected costs but also the stability of system performance across different scenarios. In addition, shortage control constraints and shortage penalties are incorporated to enhance network resilience.



The proposed model includes two objective functions: minimizing the total robust cost and minimizing the patient accessibility index to healthcare services. The model is solved using the ε-constraint method in GAMS with the CPLEX solver. Numerical experiments and sensitivity analyses demonstrate that service-level policies and the degree of model conservatism significantly affect total cost, the number of active centers, and service shortages. The findings indicate that integrating healthcare facility location decisions with medical equipment supply planning can substantially improve the resilience of healthcare systems.