Maximizing Shift Preference for Nurse Rostering Schedule Using Integer Linear Programming and Genetic Algorithm

This study explores how scheduling methods can support work-life balance and overall job satisfaction by considering the preferences of the nursing staff. Creating a nurse rostering schedule that maximizes staff preferences for working shifts, off days, and hospital demands was the main goal. A Google Form that was distributed to the nursing staff is used to gather preference data. With the help of the LPSolve IDE, an integer linear programming (ILP) technique is used for the first datasets, and the Flexible Shift Scheduling System is utilized to facilitate the use of a genetic algorithm approach for the second dataset. The first dataset's result reveals that the proposed schedule's preference weight is 205.8 (73.35%), indicating an increase of 46.24 (16.48%) over the current schedule's 159.56 (56.87%) preference weight. According to the results of the second dataset, the preference weight for the current schedule is 589 (62.98%), whereas the preference weight for the proposed schedule is 619.2 (66.21%), indicating a 30.2 (3.23%) increase. This demonstrates that both proposed schedules have higher preference weight values than the current schedule, which satisfies the study's primary goal of optimizing staff preferences. The genetic algorithm is used in the second dataset since it has a high complexity problem and can produce a near-optimal solution. Flexible Shift Scheduling System generates quicker and easier schedules compared to manual schedules. This study emphasizes the importance of including nurse staff preferences into consideration when creating nurse rostering schedule procedures to support a happier and more engaged nursing team.

@article{Razali2025,
  title     = {Maximizing Shift Preference for Nurse Rostering Schedule Using Integer Linear Programming and Genetic Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Siti Noor Asyikin Binti Mohd Razali and Thesigan Achari A/L Tamilarasan and Batrisyia Binti Basri and Norazman bin Arbin},
  doi       = {10.14569/IJACSA.2025.0160570},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160570}
}