A reinforcement learning approach for the online dynamic home health care scheduling problem.

Autor: Ta-Dinh Q; ORLab, Faculty of Computer Science, Phenikaa University, Hanoi, Vietnam., Pham TS; Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Quebec, Canada., Hà MH; SLSCM, Faculty of Data Science and Artificial Intelligence, College of Technology, National Economics University, Hanoi, Vietnam. hoanghm@neu.edu.vn., Rousseau LM; Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Quebec, Canada.
Jazyk: angličtina
Zdroj: Health care management science [Health Care Manag Sci] 2024 Nov 14. Date of Electronic Publication: 2024 Nov 14.
DOI: 10.1007/s10729-024-09692-5
Abstrakt: Over recent years, home health care has gained significant attention as an efficient solution to the increasing demand for healthcare services. Home health care scheduling is a challenging problem involving multiple complicated assignments and routing decisions subject to various constraints. The problem becomes even more challenging when considered on a rolling horizon with stochastic patient requests. This paper discusses the Online Dynamic Home Health Care Scheduling Problem (ODHHCSP), in which a home health care agency has to decide whether to accept or reject a patient request and determine the visit schedule and routes in case of acceptance. The objective of the problem is to maximize the number of patients served, given the limited resources. When the agency receives a patient's request, a decision must be made on the spot, which poses many challenges, such as stochastic future requests or a limited time budget for decision-making. In this paper, we model the problem as a Markov decision process and propose a reinforcement learning (RL) approach. The experimental results show that the proposed approach outperforms other algorithms in the literature in terms of solution quality. In addition, a constant runtime of less than 0.001 seconds for each decision makes the approach especially suitable for an online setting like our problem.
(© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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