Research On Resource Allocation Of Cloud Medical System Based On Robust Optimization

With the improvement of living standard and the enhancement of health consciousness,Chinese residents have a high demand for the social healthcare services.However,people always tend to trust the tertiary hospitals that have high-quality medical resources due to the inadequate level medical system in China.This is the main reason why the large hospitals are always overcrowded,while the small hospitals are always idle.In order to solve this problem,Chinese government,healthcare industry and academia have begun to apply the Internet techniques into the healthcare system in recent years.As an emerging Internet healthcare system,cloud healthcare system can integrate the diagnosis and treatment of a patient in multiple hospitals together by using the Internet techniques,which can help utilize both the higher-quality medical resources in the large hospitals and the lower-quality medical resources in the small hospitals efficiently.Therefore,this thesis takes this cloud healthcare system as the target system and is focused on its key scientific issues that are originated from the practical demands of operations management in this new Internet healthcare system based on the theory of systems engineering and the methodologies in the field of operational research,management science,computer science,engineering mathematics and so on.The major research works in this thesis can be described as follows.First of all,through enterprise investigation and literature review,we learned about the overall process of the cloud medical system,established the overall flow chart of the system,and extracted the core issues of this thesis through the analysis of the flow chart of the system,namely the core resource allocation in the cloud medical system.This thesis establishes a preliminary deterministic model for the allocation of core resources,and conducts experimental analysis of the model.We put forward two important factors that affect resource allocation:patient treatment time and hospital referral probability.Secondly,by analyzing the cloud medical system and considering the actual medical process,we determined this core resource allocation problem as the uncertainty problem,and chose the robust optimization method to model this problem.In this thesis,two single-factor uncertain robust optimization models,which take the patient’s diagnosis and treatment time as uncertain factors and the inter-hospital referral probability as uncertain factors,are established respectively,and a series of experiments and results are conducted.Finally,in order to make the mathematical model better reflect the actual medical treatment process,this thesis established a robust optimization model on the basis of the previous mathematical model,which also took into account the patient’s diagnosis and treatment time and the inter-hospital referral probability.We carry out case design and result analysis from the two aspects of system supply and demand change and model robust parameter change,and verify the feasibility of the proposed method and the validity of the results obtained.Through the above research,we find that on the one hand,the validity of the established model is verified.On the other hand,the proposed robust optimization model and experimental analysis can provide theoretical support and specific allocation scheme for the management decision-making of the system.