Sea lion attacking‐based deer hunting optimization algorithm for dynamic nurse scheduling in health care sector contribution of hybrid algorithm in cloud
Summary The main intent of this article is to frame the dynamic NSP in the cloud using a multi‐objective optimization strategy. The benefit of the dynamic scheduling over static scheduling is that the scheduling will be done week by week based on the available number of nurses. For solving this prob...
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Veröffentlicht in: | Concurrency and computation 2022-11, Vol.34 (25), p.n/a |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Summary
The main intent of this article is to frame the dynamic NSP in the cloud using a multi‐objective optimization strategy. The benefit of the dynamic scheduling over static scheduling is that the scheduling will be done week by week based on the available number of nurses. For solving this problem, several constraints like single assignment per day, under‐staffing, shift type successions, consecutive assignments, consecutive resting days, and complete week‐end are considered. With reference to these constraints, a minimized objective function is used based on the nurse demand in week by week. Each constraint is having some conditions for solving this problem. These schedules are stored in cloud for efficient decision making regarding NSP and it helps in assisting the future scheduling purposes. It also offers secure storage when compared to the other storage devices. As a novelty, this article tries to employ the hybrid meta‐heuristic algorithm called sea lion attacking‐based deer hunting optimization algorithm. Hybrid optimization algorithms have been reported to be promising for certain search problems with a higher convergence rate. Hence, the developed hybrid optimization algorithm hardly helps to generate a feasible and near‐optimal schedule at the end of the horizon. |
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ISSN: | 1532-0626 1532-0634 |
DOI: | 10.1002/cpe.7249 |