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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 23144904 |
| DOI: | 10.1155/je/8845121 |
