Scientific Reports· 2026Q1
A dynamic family doctor rescheduling problem with integrated home health care and outpatient services
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- Q1SCImago
- 2026year
Short summary
A novel mixed-integer programming model and a hybrid genetic-algorithm-tabu-search method efficiently reschedule family doctors dynamically, integrating home health and outpatient services under real-time demand changes.
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Key points
- Proposes a dynamic rescheduling problem for family doctors integrating home health and outpatient services.
- Decomposes the problem into initial scheduling, home care route re-optimization, and outpatient overtime scheduling using a rolling horizon framework.
- Models each subproblem as a deterministic mixed-integer programming model.
- Develops a hybrid genetic algorithm combined with tabu search for efficient problem-solving.
- Numerical experiments confirm the algorithm's efficiency in solving the problem quickly.
AI-generated from the title and abstract; the full text is not read.
Abstract
Under China’s family doctor contract service system, patients receive either home health care or outpatient service depending on their mobility and contract contents. Considering the transitions of doctors’ service modes and the occurrence of dynamic demand events in the service processes, such as new requests, service cancellations and changes in patient availability, this paper proposes a dynamic rescheduling problem for family doctors that integrates home health care and outpatient services. Through a rolling horizon framework, we decompose the problem into three categories of subproblems: initial scheduling optimization, route re-optimization for home health care services and optimization of overtime scheduling for outpatient services. At each update time point, every subproblem is established as a deterministic mixed-integer programming model. To solve this problem, a rolling horizon-based algorithm that combines hybrid genetic algorithm with tabu search is proposed. Numerical experiments demonstrate that the proposed algorithm can efficiently solve the problem within a short time. Furthermore, we illustrate the problem properties through a sample instance.
The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗
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Field: Emergency Medical Services
Emergency Medical ServicesHealth Professions