An Integrated Supply Chain Model for Predicting Demand and Supply and Optimizing Blood Distribution

Background: The blood supply chain (BSC) is crucial for providing safe and sufficient blood, but it faces numerous challenges and needs to be robust and resilient. This study provides a comprehensive model for managing and optimizing the BSC in real-world scenarios, including emergency and routine circumstances and with consideration of health equity concepts. Method: Classic time-series models are applied to predict future supply chain circumstances, addressing uncertainty in blood demand and the need for timely supply. A structured framework and medical preferences are prioritized to optimize distribution, minimize blood shortages, minimize wastage due to expiry, and maximize blood freshness. Genetic algorithms (GA) and particle swarm optimization (PSO) are used to solve mathematical models quickly and efficiently, ensuring reliable operation. Result: The model's outcomes can effectively meet the daily needs of the BSC and assist decision-makers managing blood inventory and distribution, improving robustness and resilience. Conclusions: Utilizing weights allows for the effective management of each objective function to convert the model into a single-objective mixed-integer linear programming (SO-MILP) based on unique conditions, enabling the system to self-adjust for optimal performance, boosting the sustainability of the blood supply chain, and promoting the principle of health equity under diverse real-world settings.