| Abstrakt: |
Distribution of work shifts and off days for nurses in a duty roster is a crucial task. Much effort is spent trying to produce workable and quality rosters for nurses in hospital wards. However, there are cases, such as mandatory working days per week and balanced distribution of shift types that cannot be achieved in manually generated rosters, which are still being practised today. Hence, this study focused on solving these issues arising in Nurse Rostering Problems (NRPs) strategizing on a hybrid of Ant Colony Optimization (ACO) algorithm with a hill climbing technique. The hybridization with hill climbing was aimed at fine-tuning the initial solution or roster generated by the ACO algorithm to achieve better rosters. The hybrid model was developed with the goal of satisfying hard constraints, while minimizing violations of soft constraints in such a way that fulfilled hospital rules and nurses' preferences. The real data used for the highly constrained NRP was obtained from a large Malaysian hospital. There were specifically, three main phases involved in developing the hybrid model: generating an initial roster; updating the roster through the ACO algorithm, and implementing the hill climbing to further search for a refined solution. The results showed that with a larger value of pheromone, the chances of obtaining a good solution was found with only small penalty values. This study has proven that the hybrid ACO is able to solve NRPs with good potential solutions that satisfied all four important criteria: coverage, quality, flexibility, and cost. Subsequently, the hybrid model is also beneficial to the hospital's management as nurses can be scheduled with a balanced distribution of shifts, which in turn fulfilled their preferences. [ABSTRACT FROM AUTHOR] |
