Search Results (3)

Background/Objectives: This study addresses the complex challenge of Nurse Rostering (NR) in oncology departments, a critical component of healthcare management affecting operational efficiency and patient care quality. Given the intricate dynamics of healthcare settings, particularly in oncology clinics, where patient needs are acute and unpredictable, optimizing nurse schedules is paramount for enhancing care delivery and staff satisfaction. Methods: Employing advanced Integer Programming (IP) techniques, this research develops a comprehensive model to optimise NR. The methodology integrates a variety of constraints, including legal work hours, staff qualifications, and personal preferences, to generate equitable and efficient schedules. Through a case study approach, the model’s implementation is explored within a clinical setting, demonstrating its practical application and adaptability to real-world challenges. Results: The implementation of the IP model in a clinical setting revealed significant improvements in scheduling efficiency and staff satisfaction. The model successfully balanced workload distribution among nurses, accommodated individual preferences to a high degree, and ensured compliance with work-hour regulations, leading to optimised shift schedules that support both staff well-being and patient care standards. Conclusions: The findings underscore the effectiveness of IP in addressing the complexities of NR in oncology clinics. By facilitating a strategic allocation of nursing resources, the proposed model contributes to operational excellence in healthcare settings, underscoring the potential of Operations Research in enhancing healthcare delivery and management practices. Full article

The nurse rostering problem is an important search problem that features many constraints. In a nurse rostering problem, these constraints are defined by processes such as maintaining work regulations, assigning nurse shifts, and considering nurse preferences. A number of approaches to address these constraints, such as penalty function methods, have been investigated in the literature. We propose two types of hybrid metaheuristic approaches for solving the nurse rostering problem, which are based on combining harmony search techniques and artificial immune systems to balance local and global searches and prevent slow convergence speeds and prematurity. The proposed algorithms are evaluated against a benchmarking dataset of nurse rostering problems; the results show that they identify better or best known solutions compared to those identified in other studies for most instances. The results also show that the combination of harmony search and artificial immune systems is better suited than using single metaheuristic or other hybridization methods for finding upper-bound solutions for nurse rostering problems and discrete optimization problems. Full article

In this contribution, a generic two-phase stochastic variable neighborhood approach is applied to nurse rostering problems. The proposed algorithm is used for creating feasible and efficient nurse rosters for many different nurse rostering cases. In order to demonstrate the efficiency and generic applicability of the proposed approach, experiments with real-world input data coming from many different nurse rostering cases have been conducted. The nurse rostering instances used have significant differences in nature, structure, philosophy and the type of hard and soft constraints. Computational results show that the proposed algorithm performs better than six different existing approaches applied to the same nurse rostering input instances using the same evaluation criteria. In addition, in all cases, it manages to reach the best-known fitness achieved in the literature, and in one case, it manages to beat the best-known fitness achieved till now. Full article