Home health care routing and scheduling problems considering patient classification and outsourcing: Modeling and a solution algorithm

• Published in: Computers & operations research Vol. 182; p. 107143
• Main Authors: Ma, Xiaomeng, Pu, Xujin, Fu, Yaping, Gao, Kaizhou, Xu, Yuchen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2025
• Subjects: Home health care; Metaheuristic algorithms; Multi-objective optimization; Q-learning; Routing and scheduling; Q-learning; Multi-objective optimization; Routing and scheduling; Home health care; Metaheuristic algorithms
• ISSN: 0305-0548
• DOI: 10.1016/j.cor.2025.107143

Home health care (HHC) is treated as a substitute for hospitalization and plays a crucial role in relieving the pressure of medical resources resulting from population aging. HHC routing and scheduling problems have received much attention in modeling and optimization fields. This paper proposes a multi-objective HHC routing and scheduling problem considering patient classification and outsourcing operation, in which patients are classified into two types, i.e., VIP patients and ordinary patients. All the patients are assigned to an HHC center or outsourced to third-party service providers. Then, the caregivers in the HHC center are scheduled to provide services for the assigned patients. First, a mixed integer programming model with minimizing total operation cost and minimizing total tardiness is established. Second, a Q-learning-based multi-objective evolutionary algorithm with problem-specific knowledge (QMEA-K) is specially devised. At last, numerous experiments are carried out by making comparisons between QMEA-K and four algorithms and an exact solver CPLEX. The acquired results prove the effectiveness and advantages of QMEA-K in tackling the concerned problem.