Abstract
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.
Highlights
The problem of staff scheduling has been studied extensively over the past several decades [1]
Based on the advantage of the hybrid methods proposed in the literature [23,24], we propose a hybrid approach that involves the use of harmony search (HS) and artificial immune systems (AIS), both of which are well-known population-based metaheuristics (P-metas)
We approach HHSAIS to solve Nurse rostering problems (NRPs) because, even though the new solution generated from HS procedure is not better than the worst existing harmony memory (HM), we expect that AIS can make it a better solution by searching neighbor solutions through cloning and mutation operator
Summary
The problem of staff scheduling has been studied extensively over the past several decades [1]. Burke et al [18] hybridized a steepest-descent improvement with a genetic algorithm and demonstrated that this hybridization was an adequate approach for solving NRPs. Awadallah et al [19] proposed a hybridized approach for the application of the hill climbing optimization method to an artificial bee colony. The advantages of a hybrid approach involving HS and AIS in solving optimization problems include the fact that HS is an emerging algorithm for swarm intelligence optimization and heuristic global search algorithms. This approach generates a new individual via cooperation among individuals, and its local searching ability is enhanced by fine-tuning the mechanism employed in HS.
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