Akademik Veri Yönetim Sistemi
Computers and Industrial Engineering, cilt.187, 2024 (SCI-Expanded, Scopus)
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.