Name
Abstract | ||
|---|---|---|
Staff scheduling at hospitals is a widely-studied area in both, operation research and management science because of cost effectiveness that is required from hospitals. There is an interest for procedures on how to run a hospital more economically and efficiently. The goal of nurse scheduling is to minimize the cost of the staff and maximizing their preferences. This paper is focused on a new strategy based on hybrid model for detecting the best solution in nurse scheduling problem. The new proposed hybrid approach is obtained by combining case-based reasoning and general linear empirical model with arbitrary coefficients. The model is tested with original real world dataset obtained from the Oncology Institute of Vojvodina in Serbia. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59650-1_60 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2017 |
Keywords | Field | DocType |
Nurse scheduling problem, Case-based reasoning, General linear empirical model | Scheduling (computing),Computer science,Operations research,Nurse scheduling problem,Artificial intelligence,Case-based reasoning,Machine learning | Conference |
Volume | ISSN | Citations |
10334 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Svetlana Simic | 1 | 40 | 12.78 |
| Dragan Simic | 2 | 40 | 12.78 |
| Dragana Milutinovic | 3 | 5 | 1.87 |
| Jovanka Dordevic | 4 | 2 | 0.75 |
| Svetislav Simic | 5 | 2 | 4.13 |