Integration routing and scheduling for multiple home health care centers using a multi-objective cooperation evolutionary algorithm with stochastic simulation

被引:30
|
作者
Ma, Xiaomeng [1 ]
Fu, Yaping [1 ]
Gao, Kaizhou [2 ,3 ]
Sadollah, Ali [4 ]
Wang, Kai [1 ]
机构
[1] Qingdao Univ, Sch Business, Qingdao 266071, Peoples R China
[2] Macau Univ Sci & Technol, Inst Syst Engn, Taipa 999078, Macau, Peoples R China
[3] Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Taipa 999078, Macau, Peoples R China
[4] Univ Sci & Culture, Dept Mech Engn, Tehran, Iran
基金
中国博士后科学基金;
关键词
Home health care; Routing and scheduling; Multi -objective optimization; Cooperation evolutionary algorithm; Stochastic simulation; MATHEURISTIC APPROACH; MEMETIC ALGORITHM; OPTIMIZATION; SERVICE; PREFERENCE; DEMAND; SEARCH; MOEA/D; TRAVEL; MODEL;
D O I
10.1016/j.swevo.2022.101175
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Currently, population aging has aroused much concern in many countries since the elderly occupy a lot of social public resources in hospitals and nursing homes. Home health care (HHC) is treated as an alternative solution to serve the elderly community. In recent years, managing and organizing the operation of HHCs receive a great deal of attention. The HHC routing and scheduling problems attract huge amounts of interest from modeling and optimization areas. Nevertheless, there are rarely studies focusing on the cooperation of multiple HHC centers in such problems. This work addresses a multi-center, multi-objective and stochastic HHC routing and scheduling problem for minimizing the total operation cost and penalty cost incurred by earliness and delay service, where the caregivers' working time, customers' requirements and resource constraints are considered. Firstly, a multi -objective chance-constrained programming model is developed to formulate the studied problem. Secondly, a multi-objective cooperation evolutionary algorithm by using stochastic simulation is specially developed, in which two populations respectively perform global and local searches, and the cooperation of two populations is designed. The stochastic simulation approach is employed to evaluate the quality and feasibility of the obtained solutions. Finally, extensive experiments are performed on a set of test instances and three multi-objective optimization algorithms are compared to verify the performance of the proposed algorithm. The comparisons and discussions validate the competitiveness of the designed approach.
引用
收藏
页数:20
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