Healthcare facilities require a significant amount of resources and the effective management of these resources to ensure efficient healthcare delivery. Rostering and scheduling of nurse shifts in healthcare facilities is a significant challenge facing healthcare facilities across the globe. This work investigates the application of the tissue-like P systems (TLPS) algorithm to effectively solve complex NRP. Conventional solutions to nurse rostering problems (NRP) are continually challenged by factors such as suboptimal schedules stemming from challenges such as comprehensive and competing constraints satisfaction. The proposed NRP algorithm is based on the principles of membrane computing, which uses the principles of object shifts between membranes using communicated rules. The proposed approach was designed, implemented, and applied to the National University of Malaysia Medical Center’s nurse scheduling context for a period of 14 days. Results of the experiments carried out show a significant improvement in scheduling adequacy with adherence to all nurse rostering requirements. Results of the experiment demonstrate reduced total penalty costs involved in the use of conventional NRP algorithms. Experimental results also show that the proposed TLPS produces better quality rosters compared with other algorithms, such as the genetic algorithm (GA) and harmony search algorithm (HSA) that have been previously proposed for the National University of Malaysia Medical Center’s dataset. This study presents a robust, scalable, and feasible solution to NRP that supports adequate healthcare management.
Sharif et al. (Fri,) studied this question.