A new model and a Monte Carlo based Particle Swarm Optimization algorithm for the stochastic blood assignment problem

Blood and its products, in some cases, are the only vital and sanative medicine for the patients. Each donated blood unit is a valuable asset to protect the patients’ lives, and it should be avoided waste and non-optimal consumption. The assignment of blood and its products to hospitals is one of assignment problems, in which finding the optimal solution can lead to a reduction in mortality and waste of expenditure. In this research, a new model for the assignment of blood products in a stochastic environment is presented. The goal of the model is to minimize the preparation, deficiency and waste cost of blood products, while considering the constraints of the problem. The stochastic model is implemented in a real case and is solved by the Monte Carlo simulation method. Then, a random model is settled in a real problem in Yazd city and it is solved via a Monte Carlo based Particle Swarm Optimization algorithm. The results reveal that the solution of the hybrid algorithm can significantly reduce the costs of preparation, deficiency and waste of blood products.

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