Nurse Scheduling And Route Optimization In Home Care Service Considering Uncertainty

The deepening of the aging of the population and the increasing demand for nursing care from the elderly population have greatly increased the social demand for medical services.However,medical resources are very limited.There is a huge contradiction between the rapidly growing demand for medical care and the limited supply of medical resources.Here comes to a problem that how can we balance the relationship between them.At present,home care has been gradually adopted as an effective means to solve this problem,which originated in Europe.In order to meet the needs of patients and elderly people with disabilities(such as medical treatment,cleaning,grocery shopping,etc.),social public institutions or some medical companies organize nurses with appropriate skills to provide appropriate medical and social services to the patients in their home.Home care enables patients to receive medical services at home,and at the same time it can effectively alleviate the occupancy of hospital resources by these patients and reduce the burden of the medical system.At the operational level,home care involves many optimization problems,such as the scheduling problem of medical staff,the matching problem between medical staff and customers,and the path planning problem of medical staff.How to schedule and plan medical staff to minimize costs and reduce expenditure is the key issue in the research of home care.At the same time,there are many uncertainties in the actual scene,which will have a certain impact on the scheduling and routing of medical staff.Therefore,this paper extracts two main stochastic factors from realistic scenarios,i.e.uncertainty of travel time of medical staff and uncertainty of patient's demand,and establishes two two-stage stochastic programming models with one week as planning cycle.Because of the complexity of the stochastic programming model itself and the large scale of the optimization problem of home care in the actual scene(the large number of medical staff and patients),it is difficult to solve the model.Therefore,a hybrid genetic algorithm is designed.At the same time,in order to test the performance of the algorithm,based on the actual situation,this paper designs a total of 30 data sets as test cases(there is no open data set that can be used as a reference in the field of home care optimization),and tests the performance of the algorithm on large and small scale problems respectively.By comparing the performance of genetic algorithm and Gurobi solver on the same data set,it is found that genetic algorithm not only guarantees the optimality of the solution results,but also has great advantages in computing time.