84. Outpatient appointment scheduling
Invited abstract in session FA-12: Scheduling in Healthcare II, stream Health Care Management.
Friday, 8:45-10:15Room: H10
Authors (first author is the speaker)
| 1. | Tibor Dulai
|
| Department of Computer Science and Systems Technology, University of Pannonia | |
| 2. | Gombás Veronika
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| Department of Computer Science and Systems Technology, University of Pannonia | |
| 3. | Péter Scsibrán
|
| Department of Computer Science and Systems Technology, University of Pannonia | |
| 4. | Ágnes Vathy-Fogarassy
|
| Department of Computer Science and Systems Technology, University of Pannonia |
Abstract
Outpatient appointment scheduling plays a critical role in ensuring timely access to care for patients and efficient utilization of healthcare resources. The problem becomes especially complex when multiple examination types must be scheduled for a single patient, each requiring assignment to time slots of qualified practitioners. While exact methods can solve this problem optimally, when the number of available time slots is small, their computational time grows rapidly with the problem size. Heuristic approaches can provide high-quality solutions within reasonable time. A domain-adapted genetic algorithm was developed to address the outpatient scheduling problem, taking into account the requirements of both patients and practitioners. A custom solution encoding is used, and the mutation genetic operator was developed to best fit the objective function. The effectiveness of the algorithm was evaluated both on different calendar configurations and on real data from Hungarian healthcare providers. Our results show that the developed genetic algorithm provides optimal or near-optimal results faster, compared to the applied exact method in the case of medium- and big-sized problems.
This work was supported by the 2020-1.1.2-PIACI-KFI-2020-00045 ('Collaborative Hospital Information Platform,' Development of a process-oriented medical system to support inpatient specialized care) project with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund. Project no. MEC_R 149182 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the MEC_24 funding scheme.
Keywords
- Scheduling
- Health Care
- Metaheuristics
Status: accepted
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