A knowledge-based multi-objective evolutionary algorithm for solving home health care routing and scheduling problems with multiple centers

Nowadays, population aging is becoming more and more serious. The elderly occupies a great many of public medical resources, which brings great pressure to the public and governments. Home health care (HHC) is treated as an alternative answer to hospitalization, and plays positive roles in alleviating the stress from the shortage of medical resources incurred by the population aging. Hence, how to effectively manage and organize the operations of HHCs becomes an all-important problem in reality. HHC routing and scheduling problems attract a great deal of interest from modeling and optimization areas. Nevertheless, most of the existing studies just focus on the HHC routing and scheduling problems with a single HHC center. This study focuses on a multi-objective HHC routing and scheduling problem with multiple centers for minimizing total service cost and total tardiness incurred by delay service while meeting caregivers’ workload and resource constraints. To cope with it, we establish a mixed integer programming model to formulate the concerned problem. Then, a knowledge-based multi-objective evolutionary algorithm (KMoEA) is specially designed by employing two local search operators with the properties of the problem. Via comparing KMoEA with five algorithms and a mathematical programming solver (CPLEX), we validate that KMoEA is an effective approach for solving the studied problem.