Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization

The overall healthcare system has been prioritized within development top lists worldwide.Since many national populations are aging,combined with the availability of sophisticated medical treatments,healthcare expenditures are rapidly growing.Blood banks are a major component of any healthcare system,which store and provide the blood products needed for organ transplants,emergency medical treatments,and routine surgeries.Timely delivery of blood products is vital,especially in emergency settings.Hence,blood delivery process parameters such as safety and speed have received attention in the literature,as well as other parameters such as delivery cost.In this paper,delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization.A solution is proposed based on Deep Reinforcement Learning(DRL)to address the formulated delivery functions as Multi-objective Optimization Problems(MOPs).The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set,where each one of these sub-problems is modeled as a separate Neural Network(NN).The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training algorithm.The optimization step for the subproblems is undertaken collaboratively to optimize the overall model.Paretooptimal solutions can be directly obtained using the trained NN.Specifically,the multi-objective blood bank delivery problem is addressed in this research.Onemajor technical advantage of this approach is that once the trainedmodel is available,it can be scaled without the need formodel retraining.The scoring can be obtained directly using a straightforward computation of the NN layers in a limited time.The proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to othermulti-objective optimizationmethods.The model was trained and tested on 5 major hospitals in Saudi Arabia’s Riyadh region,and the simulation results indicated that time and cost decreased by 35%and 30%,respectively.In particular,the proposed model outperformed other state-of-the-art MOP solutions such as Genetic Algorithms and Simulated Annealing.

Management strategy of prevention and control of nosocomial infection of COVID-19 in blood purification center

There are many patients in the blood purification center who need maintenance hemodialysis to maintain life. Those patients generally havelow resistance and are easily exposed to coronavirus because they go back and forth the hospital and residence three times a week andclosely contact with family, caregivers, community personnel, people in various means of transportation, medical staff, and other patientsvisiting hospital. Therefore, the blood purification center has become a high‑risk environment for the spread of COVID-19 infection. In viewof this, our center quickly responded to the formulation and implementation of infection prevention and control measures suitable for thecharacteristics of the blood purification center and continuous renal replacement therapy (CRRT) emergency plan for fever and suspectedpatients. According to these measures, we have a positive effect on preventing and controlling nosocomial infection in the blood purificationcenter.