A multi-objective model for a nurse scheduling problem by emphasizing human factors
Assigning nurses to appropriate departments and work shifts based on human factors can strengthen teamwork and boost the efficiency of healthcare systems. The human factors considered in this study include skill, preference, and compatibility of nurses. In this regard, a unique multi-objective mathe...
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| Vydáno v: | Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Ročník 234; číslo 2; s. 179 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
01.02.2020
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| Témata: | |
| ISSN: | 2041-3033, 2041-3033 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
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| Shrnutí: | Assigning nurses to appropriate departments and work shifts based on human factors can strengthen teamwork and boost the efficiency of healthcare systems. The human factors considered in this study include skill, preference, and compatibility of nurses. In this regard, a unique multi-objective mathematical model for nurse scheduling is proposed in this article, in which nurses' decision-making styles are taken into account. Three objectives, including minimization of the total cost of staffing, minimization of the sum of incompatibility among nurses' decision-making styles assigned to the same shift days, and maximization of the overall satisfaction of nurses for their assigned shifts, are addressed in this model. Three meta-heuristics, namely, multi-objective Keshtel algorithm, non-dominated sorting genetic algorithm II, and multi-objective tabu search, are developed to solve the problem. Moreover, a data envelopment analysis method is employed to rank the obtained Pareto solutions. Afterwards, a real-life case at a large hospital in Tehran, Iran, is investigated. Eventually, the applicability and effectiveness of the proposed model are assessed based on the experimental results. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2041-3033 2041-3033 |
| DOI: | 10.1177/0954411919889560 |