A Particle Swarm Optimization‐Driven Simulation Framework for Multistage Outpatient Appointment Scheduling With Walk‐Ins and No‐Shows.
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| Title: | A Particle Swarm Optimization‐Driven Simulation Framework for Multistage Outpatient Appointment Scheduling With Walk‐Ins and No‐Shows. |
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| Authors: | Anzum, Kazi Md. Tanvir1 (AUTHOR) tanvir@iem.kuet.ac.bd, Islam, Md. Saiful1 (AUTHOR), Parvez, Mahmud1 (AUTHOR), Rahman, Md. Mahbubur2 (AUTHOR), Haddad, Assed Naked (AUTHOR) assed@poli.ufrj.br |
| Source: | Journal of Engineering (2314-4912). 6/21/2026, Vol. 2026, p1-27. 27p. |
| Subjects: | Particle swarm optimization, Discrete event simulation, Health services administration, Outpatient medical care, Patient dropouts, Operations management, Multi-objective optimization |
| Abstract: | Outpatient clinics frequently experience delays because walk‐in arrivals and patient no‐shows occur unpredictably, disrupting patient flow and limiting the efficient use of clinical resources. This study improves appointment scheduling under such uncertainty by reducing the time patients spend in the clinic and increasing the number of patients served. We developed the walk‐in and no‐show patient appointment scheduling system (WINS‐PASS), which integrates particle swarm optimization (PSO) with discrete‐event simulation (DES). The hybrid framework evaluates candidate appointment templates under random walk‐ins, no‐show behavior, and variable service times, and it generates a strong Pareto frontier that balances two objectives: minimizing total service time (TST) and maximizing the number of patients served (NOP). Across booking volumes, the PSO‐based schedules reduce the aggregate TST by approximately 17%–26% and increase daily throughput by approximately 7%–13%, without overtime. The framework also outperforms three established multiobjective algorithms: SPEA2 attains 170 min of TST with 31 patients, NSGA‐II attains 175 min with 29 patients, and IBEA attains 180 min with 22 patients, whereas PSO achieves the best‐balanced outcome of 151 min with 36 patients. Pairwise Wilcoxon rank‐sum tests over 100 replications confirm that these differences are statistically significant. Sensitivity analysis shows that morning walk‐ins improve patient flow, moderate no‐show rates support stable operation, and the mean service time strongly influences efficiency. These findings help clinics design reliable daily schedules that reduce delays and improve patient access while using existing resources more effectively, without additional staffing or overtime. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194753895 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Particle Swarm Optimization‐Driven Simulation Framework for Multistage Outpatient Appointment Scheduling With Walk‐Ins and No‐Shows. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Anzum%2C+Kazi+Md%2E+Tanvir%22">Anzum, Kazi Md. Tanvir</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tanvir@iem.kuet.ac.bd</i><br /><searchLink fieldCode="AR" term="%22Islam%2C+Md%2E+Saiful%22">Islam, Md. Saiful</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Parvez%2C+Mahmud%22">Parvez, Mahmud</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rahman%2C+Md%2E+Mahbubur%22">Rahman, Md. Mahbubur</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haddad%2C+Assed+Naked%22">Haddad, Assed Naked</searchLink> (AUTHOR)<i> assed@poli.ufrj.br</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+%282314-4912%29%22">Journal of Engineering (2314-4912)</searchLink>. 6/21/2026, Vol. 2026, p1-27. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+event+simulation%22">Discrete event simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Health+services+administration%22">Health services administration</searchLink><br /><searchLink fieldCode="DE" term="%22Outpatient+medical+care%22">Outpatient medical care</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+dropouts%22">Patient dropouts</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+management%22">Operations management</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Outpatient clinics frequently experience delays because walk‐in arrivals and patient no‐shows occur unpredictably, disrupting patient flow and limiting the efficient use of clinical resources. This study improves appointment scheduling under such uncertainty by reducing the time patients spend in the clinic and increasing the number of patients served. We developed the walk‐in and no‐show patient appointment scheduling system (WINS‐PASS), which integrates particle swarm optimization (PSO) with discrete‐event simulation (DES). The hybrid framework evaluates candidate appointment templates under random walk‐ins, no‐show behavior, and variable service times, and it generates a strong Pareto frontier that balances two objectives: minimizing total service time (TST) and maximizing the number of patients served (NOP). Across booking volumes, the PSO‐based schedules reduce the aggregate TST by approximately 17%–26% and increase daily throughput by approximately 7%–13%, without overtime. The framework also outperforms three established multiobjective algorithms: SPEA2 attains 170 min of TST with 31 patients, NSGA‐II attains 175 min with 29 patients, and IBEA attains 180 min with 22 patients, whereas PSO achieves the best‐balanced outcome of 151 min with 36 patients. Pairwise Wilcoxon rank‐sum tests over 100 replications confirm that these differences are statistically significant. Sensitivity analysis shows that morning walk‐ins improve patient flow, moderate no‐show rates support stable operation, and the mean service time strongly influences efficiency. These findings help clinics design reliable daily schedules that reduce delays and improve patient access while using existing resources more effectively, without additional staffing or overtime. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/je/8845121 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1 Subjects: – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Discrete event simulation Type: general – SubjectFull: Health services administration Type: general – SubjectFull: Outpatient medical care Type: general – SubjectFull: Patient dropouts Type: general – SubjectFull: Operations management Type: general – SubjectFull: Multi-objective optimization Type: general Titles: – TitleFull: A Particle Swarm Optimization‐Driven Simulation Framework for Multistage Outpatient Appointment Scheduling With Walk‐Ins and No‐Shows. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Anzum, Kazi Md. Tanvir – PersonEntity: Name: NameFull: Islam, Md. Saiful – PersonEntity: Name: NameFull: Parvez, Mahmud – PersonEntity: Name: NameFull: Rahman, Md. Mahbubur – PersonEntity: Name: NameFull: Haddad, Assed Naked IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 06 Text: 6/21/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23144904 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Engineering (2314-4912) Type: main |
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