Post-Earthquake Field Hospital Bed Capacity Allocation Problem Solving Two-Stage Stochastic Programming: A Case Study in Istanbul Avcılar

Field hospitals are temporary healthcare facilities deployed to provide medical services in scenarios where conventional healthcare infrastructure is disrupted due to natural or human-induced disasters, migration, armed conflicts, or similar crises. Depending on the anticipated needs, these facilities may be categorized into specialized types, such as orthopedic and traumatology field hospitals, surgical field hospitals, or general medical aid field hospitals. Bed capacity is a critical determinant of the scale of a field hospital, which in turn influences the spatial requirements for setup, personnel allocation, and associated costs. Typically established for short-term use, ranging from six months to two years, the operational duration, bed capacity, and structural configuration of field hospitals collectively define their functional capabilities. This study proposes a two-stage stochastic mathematical programming model to address the problem of calculating the optimal bed capacity for field hospitals delivering post-disaster healthcare services, with a focus on earthquake scenarios. Compared with two-stage stochastic models, the mathematical model proposed in this study exhibits distinct differences from existing models in the disaster and hospital bed capacity modeling literature. The model integrates uncertainties inherent in disaster contexts, such as fluctuating patient influx and resource availability, to optimize decision-making for bed allocation. The proposed framework is applied to Avcılar, a district within Istanbul, Türkiye, identified as a high-risk zone for seismic activity due to its proximity to the North Anatolian Fault. By leveraging geospatial and demographic data, the model aims to enhance preparedness and operational efficiency of field hospitals in mitigating healthcare service disruptions following a major earthquake. The results underscore the utility of stochastic optimization in resource planning under uncertainty, offering actionable insights for policymakers and disaster response coordinators.