A cluster-based algorithm for home health care planning: A case study in Australia. (October 2022) - Pahlevani, Delaram; Abbasi, Babak; Hearne, John W.; Eberhard, Andrew
Abstract: Home health care (HHC) refers to the delivery of social, medical and paramedical services to people in their homes. Caregivers are assigned and routed to perform various tasks such as personal care and household chores at the client's homes. Minimising the total cost and satisfying the client requirements and preferences are critical in HHC. In this paper, we present a mixed-integer linear programming model for HHC routing and scheduling problems, which considers fair and balanced workload allocation of caregivers while minimising the total cost and addressing the client's needs. Due to the complexity of the problem, using a commercial solver is not practical for real size instances. Therefore, we develop a multi-steps clustering approach using Ordering Points and Agglomerative Hierarchical Clustering to solve the model. The proposed solution approach is applied on several test instances to examine its performance. We also apply the proposed model and solution approach on a case study in Australia. Highlights: Home health care planning problem with practical constraints is considered. Clients and caregivers' satisfaction and balanced workload are considered. A cluster-based framework is introduced to solve large-sized instances. A standard benchmark for home care planning problem with preferences is generated. Extensive numerical experiments based on real-life data are presented.