Research On Multi-objective Home Health Care Scheduling And Path Optimization With Multiple Care Centers

With the gradual deepening of population aging,the medical care of the elderly has been widely concerned by all sectors of society.Home health care is a new type of medical care model.According to the service needs of the patient(or the elderly),home health care aims to designate nursing staff to carry necessary medical equipment and drugs to the patient’s residence to provide medical examination,rehabilitation care and life care services.Home health care can not only effectively alleviate the shortage of public medical resources,but also provide more efficient services according to the actual needs of patients.In the process of home health care services,decision makers usually need to solve the problem of assigning nursing staff to patients and planning paths for nursing staff,that is,home health care scheduling and path optimization problem.Reasonable scheduling of nursing staff and optimization of service order and path are of great significance to reduce the cost of home health care services and improve service quality.Generally,multiple care centers need to coordinate and cooperate to provide medical services for patients.Therefore,multi-objective home health care scheduling and path optimization problems with multiple care centers were studied.The specific research contents are as follows:1)This paper studies the scheduling and path optimization of multi-center and multi-objective home health care with workload balance.On the premise of meeting the skill requirements of patients,workload balance of nursing staff and resource constraints,a mixed integer programming model is established to minimize service costs and delay service time.According to the characteristics of the problem,four properties are derived and a knowledge-based multi-objective evolutionary algorithm is designed to solve the problem.In order to verify the performance of the designed algorithm in solving the problem,it is compared with nondominated sorting genetic algorithm II,multi-objective evolutionary algorithm based on decomposition and improved multi-objective artificial bee colony algorithm on a set of test instances.The experimental results prove the feasibility and superiority of the proposed algorithm.2)This paper studies the scheduling and path optimization of multi-center and multi-objective home health care with uncertainty characteristics.In order to further ensure the feasibility of scheduling decision,the patient’s time window,stochastic service time and travel time are included in the problem.On the basis of fully considering the characteristics of the studied problem,a chance-constrained programming model is established with the goal of minimizing the operation cost and the penalty cost caused by violating the patient’s time window,and the working time of nursing staff as the constraint.Based on the characteristics of the problem,a multiobjective coevolutionary algorithm is proposed and combined with stochastic simulation method to solve the problem.By comparing the designed algorithm with nondominated sorting genetic algorithm II,multi-objective evolutionary algorithm based on decomposition and improved multi-objective artificial bee colony algorithm on a set of test instances,the experimental results prove the feasibility and effectiveness of the proposed algorithm.3)This paper studies the scheduling and path optimization of multi-center and multi-objective home health care with location decisions.The location decision is introduced,and the opening cost of the home health care center,the human cost of nursing staff and the appointment time of patients are also considered.According to the characteristics of the problem,a mixed integer programming model is established to minimize the operation cost and delay service time.On the basis of fully analyzing the characteristics of the problem,a multi-objective artificial bee colony algorithm with problem-specific knowledge is proposed,and it is compared with the nondominated sorting genetic algorithm II,multi-objective evolutionary algorithm based on decomposition and multi-objective particle swarm optimization algorithm on a set of test instances.The experimental results show the effectiveness and superiority of the proposed algorithm.To sum up,this paper studies home health care scheduling and path optimization problems from the aspects of optimization objectives,problem characteristics,and other aspects.At the same time,it deeply studies the solution methods of the problem model with both random characteristics and complex constraints.The intelligent optimization method is used to search for solutions,and the stochastic simulation method is used to solve the uncertainty in the service process and evaluate the feasibility of solutions.By applying the above methods,high-quality solutions can be obtained.The model and the solution method in this paper provide important theoretical guidance and method reference for managers and decision makers of home health care.