Outpatient Scheduling with Genetic Algorithm: The Power of Mutation Operators.
Background: Outpatient scheduling is a complex and time-consuming task. To address this challenge, numerous studies have developed various optimization methods, including genetic algorithms. Objectives: This study aims to develop a task-specific genetic algorithm and investigate the effect of differ...
| Publicado en: | Studies in Health Technology & Informatics Vol. 324; pp. 258 - 264 |
|---|---|
| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184908403&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184908403 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 324 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 184908403 184908403 184908403 10.3233/SHTI250198 184908403 ppf: 258 ppct: 6 formats: tig: atl: Outpatient Scheduling with Genetic Algorithm: The Power of Mutation Operators. aug: au: GOMBÁS, Veronika SCSIBRÁN, Péter Bálint DULAI, Tibor VATHY-FOGARASSY, Ágnes affil: Department of Computer Science and Systems Technology, University of Pannonia, Veszprem, Hungary sug: subj: Outpatient Service Appointments and Schedules Appointment and Scheduling Information Systems Evaluation Algorithms Evaluation Human Funding Source Comparative Studies Almanacs Software Design ab: Background: Outpatient scheduling is a complex and time-consuming task. To address this challenge, numerous studies have developed various optimization methods, including genetic algorithms. Objectives: This study aims to develop a task-specific genetic algorithm and investigate the effect of different mutation operators on its performance, focusing on minimizing the earliest completion time of scheduled examinations. Methods: Random and two heuristic mutation operators were designed and compared. The effect of these mutation operators and their parameters were evaluated across four fundamentally distinct scheduling scenarios. Results: The exponential mutation operator outperformed all others across all scheduling problems. It achieved an optimal schedule in 100% of runs for the simplest task and in 74.5% of runs for the most complex one. In comparison, the random mutation operator achieved 100% and 1%, while the polynomial operator reached 75.66% and only 0.22%, respectively. Conclusion: The efficiency of the genetic algorithm developed for outpatient scheduling is strongly influenced by the choice of mutation operator. The performance of the algorithm can be greatly enhanced by employing a specialized mutation operator tailored to the objective function. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|