Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO)
Telemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artific...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6349 |
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| author | Deborah Tshiamala Lagouge Tartibu |
| author_facet | Deborah Tshiamala Lagouge Tartibu |
| author_sort | Deborah Tshiamala |
| collection | DOAJ |
| description | Telemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) model aimed at improving the performance of telemedicine queuing systems. A simulation-based dataset was generated to represent patient arrivals, service rates, and queuing behaviors. An ANN was trained to predict key performance metrics, including queue intensity, system utilization, and delays. To further enhance the model’s predictive capabilities, PSO was applied to optimize critical ANN parameters, such as neuron count, swarm size, and acceleration factors. The optimized ANN-PSO model achieved high predictive accuracy, with correlation coefficients (R<sup>2</sup>) consistently exceeding 0.90 and low mean squared errors across most outputs. The findings show that optimal parameter configurations vary depending on the specific performance metric, reinforcing the value of adaptive optimization. The results confirm the ANN-PSO model’s ability to accurately predict queuing behavior and optimize system performance, providing a practical decision-support tool for telemedicine administrators to dynamically manage patient flow, reduce waiting times, and enhance resource utilization under variable demand conditions. |
| format | Article |
| id | doaj-art-182d28149e4c4ec694bef36d0c19618c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-182d28149e4c4ec694bef36d0c19618c2025-08-20T02:23:08ZengMDPI AGApplied Sciences2076-34172025-06-011511634910.3390/app15116349Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO)Deborah Tshiamala0Lagouge Tartibu1Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg 2028, South AfricaDepartment of Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg 2028, South AfricaTelemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) model aimed at improving the performance of telemedicine queuing systems. A simulation-based dataset was generated to represent patient arrivals, service rates, and queuing behaviors. An ANN was trained to predict key performance metrics, including queue intensity, system utilization, and delays. To further enhance the model’s predictive capabilities, PSO was applied to optimize critical ANN parameters, such as neuron count, swarm size, and acceleration factors. The optimized ANN-PSO model achieved high predictive accuracy, with correlation coefficients (R<sup>2</sup>) consistently exceeding 0.90 and low mean squared errors across most outputs. The findings show that optimal parameter configurations vary depending on the specific performance metric, reinforcing the value of adaptive optimization. The results confirm the ANN-PSO model’s ability to accurately predict queuing behavior and optimize system performance, providing a practical decision-support tool for telemedicine administrators to dynamically manage patient flow, reduce waiting times, and enhance resource utilization under variable demand conditions.https://www.mdpi.com/2076-3417/15/11/6349artificial neural networks (ANNs)particle swarm optimization (PSO)telemedicinequeuing systems |
| spellingShingle | Deborah Tshiamala Lagouge Tartibu Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) Applied Sciences artificial neural networks (ANNs) particle swarm optimization (PSO) telemedicine queuing systems |
| title | Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) |
| title_full | Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) |
| title_fullStr | Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) |
| title_full_unstemmed | Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) |
| title_short | Telemedicine Queuing System Study: Integrating Queuing Theory, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) |
| title_sort | telemedicine queuing system study integrating queuing theory artificial neural networks anns and particle swarm optimization pso |
| topic | artificial neural networks (ANNs) particle swarm optimization (PSO) telemedicine queuing systems |
| url | https://www.mdpi.com/2076-3417/15/11/6349 |
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