| dc.creator |
URGANCI, Kemal Burak |
|
| dc.creator |
Gülmez, Esra |
|
| dc.creator |
Aydin, Mehmet Emin |
|
| dc.creator |
KORUCA, Halil İbrahim |
|
| dc.date |
2024-01-01T00:00:00Z |
|
| dc.date.accessioned |
2025-02-25T10:23:03Z |
|
| dc.date.available |
2025-02-25T10:23:03Z |
|
| dc.identifier |
6116b290-51bd-4f7d-a0be-61a18ace5e7c |
|
| dc.identifier |
10.1016/j.cie.2023.109857 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/6116b290-51bd-4f7d-a0be-61a18ace5e7c/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/99921 |
|
| dc.description |
Employee satisfaction significantly influences the success of business. This emphasises on the importance of employees managing their work, family and personal lives to maintain their physical and mental well-being. This is especially crucial in health-care sector, where physical and mental well-being directly affects the quality of out-coming services provided. Work-life balance, defined as the challenge of striking a reasonable equilibrium between work, family, and personal life, is gaining more attention. However, many studies do not adequately consider employee preferences when addressing this issue. This study introduces a mathematical model for work-life balance problem prioritising the worker preferences focusing on healthcare workers as a special case where personnel preferences are integrated into decision-making. The model has been comparatively solved with population-based algorithms for optimising weekly personnel schedules in order to make them more suitable for work-life balance. The population-based heuristic algorithms used for optimising the schedules are swarm intelligence algorithms; namely ant colony and particle swarm optimisation algorithms. The proposed approach allows the employees to opt their working hours and periods in the work-place, flexibly. We demonstrated with comparative analysis that the produced results with swarm intelligence algorithms evidently outperform one of the state-of-art works done with genetic algorithms, which proves the strength of the proposed problem solvers. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Heuristic and swarm intelligence algorithms for work-life balance problem |
|
| dc.type |
info:eu-repo/semantics/article |
|