Abstract:

The integration of medical care and elderly care is an important elderly care model to alleviate the shortage of medical and elderly care resources in China's communities. Therefore， activating the limited medical and elderly care resources is the key to improving the quality of elderly care services. It focuses on the problem of optimizing the scheduling of service personnel in the dynamic medical and elderly care demand scenarios of community elders for this paper. By constructing a multi-agent perturbation measurement model based on prospect theory， with the maximization of the comprehensive prospect value of community institutions， service personnel， and elders as the perturbation optimization objective， a multi-objective interference management optimization model is established. An improved Grey Wolf Optimization algorithm （IGWO） embedded with genetic operators is innovatively proposed. Finally， the effectiveness of the model and algorithm in this paper is verified through a standard case set. The results show that the non-inferior solution set obtained by the improved Grey Wolf Optimization algorithm embedded with genetic operators is significantly superior to the Grey Wolf Optimization algorithm （GWO） and the multi-objective genetic algorithm （NSGA-II） in terms of distribution， dominance， and convergence indicators. A decision-making framework is provided that balances efficiency and fairness for the scheduling of medical and elderly care resources in dynamic environments. The proposed behavioral modeling method expands the application boundaries of traditional operations research in the field of public services， and the algorithm innovation provides a new tool for solving complex scheduling problems.

Key words: elderly needs, community medical and elderly care services, personnel scheduling