Spatial multi-objective optimization of institutional elderly-care facilities: A case study in Shanghai

As the population aging trend accelerates in many countries throughout the world, notably in China, elderly-care services become increasingly vital. Institutional elderly-care services, a major component of the elderly-care system, are essential for older persons who need to leave their homes and get care from trained caregivers in institutional elderly-care facilities (iECFs). Although efforts have been made to provide adequate iECFs and the capacities to fulfill the rapidly rising demand of these older individuals in many Chinese cities, there is still room for improvement in the iECFs' spatial allocation and usage efficiency. Hence, in this research, a novel multi-objective iECFs optimization (MiEO) model coupled with an improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) was proposed to help identify the locations and capacities of iECFs in order to effectively assist the iECFs-related policy making and management. The proposed MiEO-INSGA-II model was also successfully evaluated and utilized in the case study of Shanghai, demonstrating its effectiveness. Lastly, the limitations of this research were also discussed, some of which would be the direction of our future research.