Healthcare Resource and Staffing Optimization Model for Pandemic Response

Articles in Press

Authors

Faculty of Industrial Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran.

Abstract

This study presents a mathematical optimization model for resource allocation and staff management during a pandemic, focusing on balancing patient demand, facility capacity, and resource utilization. The model aims to minimize total costs, including staffing, resource procurement, and penalties for unmet demand, while ensuring efficient patient assignment and facility operation. A key feature of the model is the integration of a cross-training strategy to enhance workforce flexibility, enabling staff to perform multiple roles and helping address staffing shortages during peak demand periods. The model accounts for multiple patient types, each with distinct resource requirements, and healthcare facilities with varying capacities for beds, ventilators, and staff. The results demonstrate that the model successfully optimizes resource allocation, achieving a 14.98% improvement in resource usage efficiency and a facility utilization rate of 69.19%. Through strategic implementation of staff transfers and cross-training policies, the model maintained high operational efficiency while improving facility utilization by 0.18%. These findings highlight the significance of a flexible workforce and strategic resource management in improving healthcare resilience and responsiveness during a pandemic.

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Advances in Industrial Engineering

Articles in Press, Accepted Manuscript
Available Online from 26 January 2025

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