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| Autore principale: | |
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| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2025
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| Soggetti: | |
| Accesso online: | https://doi.org/10.5281/zenodo.18710549 |
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Sommario:
- <p>Inefficient queue management in humanitarian operations can exacerbate aid delays, heighten social tension and entrench inequity, especially for vulnerable groups who cannot withstand long waits for registration or assistance. Addressing this challenge, the present study developed and validated a computational queue management framework that simultaneously enhances operational efficiency and upholds fairness in prioritising at-risk populations.</p> <p>A discrete-event simulation modelled 1,000 synthetic beneficiaries across fourteen server configurations and sixteen queuing policies, including FIFO, LIFO, Shortest Job First (SJF), Random Order Service (ROS), Round-Robin (RR), Multi-Level Feedback-Queue (MLFQ) and 10 hybrid fairness-aware variants, within a modular architecture that supports low resource deployment and future extensibility. Classical congestion measures were integrated with a weighted multi-attribute equity score.</p>