Expert Scheduling In The Case Of Multiple Health Service Stations

Health service industry is closely related to people's daily life.After years of rapid development,it has become a sunrise industry in China.With the rapid maturity of the Internet in the new century,many new service modes have emerged in the field of health service.Especially the emergence of the Internet health service platform will integrate the scattered health service resources offline and provide users with convenient and fast health services.Expert scheduling with multiple health service stations needs to assign experts to different service stations according to user's reservation information.Under the background of multiple service stations,how to efficiently and reasonably dispatch experts to provide services is a difficult problem.Therefore,it is of great practical significance to study expert scheduling with multiple health service stations.Based on the knowledge of health services and human resources,a 0-1 integer programming model is formulated in which many factors are considered,such as users,experts,shifts,service stations and so on.The model takes user preference,consistency of expert shifts,fairness of tasks and number of scheduling experts as evaluation indexes.A two-stage evolutionary algorithm based on NSGA-II is designed.A new cultural algorithm is also introduced to construct belief space based on population space and guide population evolution through belief space.Two-stage evolutionary algorithm decomposes the problem into two phases: phase 1 is set for optimization of the assignment of experts and phase 2 is for optimization of the service queues.Cultural algorithm can dynamically collect evolutionary information of population space and guide the optimization of population space.Expert assignment knowledge and operator knowledge constitute the belief space.New algorithms TSGA-C and NSGA-II-C are developed by adding cultural algorithm framework to TSGA and NSGA-II respectively.Finally,according to the actual scale of health service platform,four groups of cases A,B,C and D are randomly generated.The experimental results of TSGA,TSGA-C,NSGA-II and NSGA-II-C are compared.Computational experiments show that TSGA can improve the quality of solutions significantly.As the scale enlarges and the constraints become complex,the additional cultural algorithm can improve the solving speed and its pareto solution set is more abundant.