Ramli, Mohamad Raziff (2015) An improved fair nurse scheduling optimisation using particle swarm intelligent technique. Masters thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
Nurse schedule is a list showing the arrangement such as dates and times of each employee must work at a particular period of time. Nurse scheduling is one of the important and complex tasks which influence the hospital productivity. Common issues in nurse scheduling problem are the unfair of the working shifts between nurses and the shortages of nursing staffs combined with the uncertain nature of patient workloads. Assigning each available nurse to the right place at the right time is therefore a major concern among many modern hospitals. A well-designed schedule algorithm shall be able to generate an efficient task that can precede the restriction and variability. Nevertheless, the fairness of the task been assigned to the nurses should also considered nurses perspectives. Therefore, this research aims to propose practical and effective nurse scheduling approach that takes into consideration both preferences by hospital and nurse. The suggested approach provides better solution not only with respect to efficiency but also the quality of the nurse scheduling to the hospital and the nurse themselves. Particle Swarm Optimisation (PSO) has many successful applications in continuous optimisation problems, thus, the capability of PSO is used to provide a high performance predictive nurse schedule. The nurse schedule produced by PSO then will investigate and compared with real schedule while the data successfully tested on benchmark and verified base on fairness measures. The experimental results have positively shown that the nurse schedule generated by PSO much better and effective in providing reasonably high quality solutions with respect to the desired hospital.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Scheduling, Computer programs, Scheduling (Management), Scheduling, Data processing, Nurses, Data processing, Nurse Scheduling, Particle Swarm Intelligent |
| Subjects: | T Technology > T Technology (General) T Technology > TS Manufactures |
| Divisions: | Library > Tesis > FTMK |
| Depositing User: | Mohd Hannif Jamaludin |
| Date Deposited: | 04 Aug 2016 04:12 |
| Last Modified: | 20 Apr 2022 11:06 |
| URI: | http://eprints.utem.edu.my/id/eprint/16854 |
| Statistic Details: | View Download Statistic |
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