Optimal collecting policy for apheresis platelets in a regional blood center

Planning platelet collection and inventory must rely not only on adequate forecasts of transfusion demand but also sophisticated mathematical modeling techniques. This research aims to develop a better demand forecasting model of apheresis platelets and a mathematical programming model to determine the best target amounts of apheresis platelet collection.

Time series data of apheresis platelets collected from donors and platelets supplied to hospitals daily in Taipei Blood Center from January 2014 to December 2015 was used to fit a forecasting model which combines a regression-type model for formulating the deterministic trends and seasonal variation and an autoregressive moving average model (ARMA) for explaining remaining serial correlations. A seasonal autoregressive integrated moving average (SARIMA) model was also used for benchmarking the prediction performance. A linear programming model was then formulated to solve for the optimal daily target collection volumes that maximize the total social benefits.

The time series model achieved good predictive power with a mean absolute percentage error less than 10%. The appropriateness of the proposed target collection volumes was also verified by using a simulation model, and the proportion of the total platelets requested by hospitals that can be filled by collected apheresis platelets can increase significantly by using the new policy.

The methods proposed in this study can be easily implemented to enhance the management efficiency of blood collecting and supplying of a blood center, and to decrease the costs of the blood outdates and shortages.