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.

Real wars involve a considerable number of uncertainties when determining firing scheduling. This study proposes a robust optimization model that considers uncertainties in wars. In this model, parameters that are affected by enemy's behavior and will, i.e., threats from enemy targets and threat time from enemy targets, are assumed as uncertain parameters. The robust optimization model considering these parameters is an intractable model with semi-infinite constraints. Thus, this study proposes an approach to obtain a solution by reformulating this model into a tractable problem; the approach involves developing a robust optimization model using the scenario concept and finding a solution in that model. Here, the combinations that express uncertain parameters are assumed by scenarios. This approach divides problems into master and subproblems to find a robust solution. A genetic algorithm is utilized in the master problem to overcome the complexity of global searches, thereby obtaining a solution within a reasonable time. In the subproblem, the worst scenarios for any solution are searched to find the robust solution even in cases where all scenarios have been expressed. Numerical experiments are conducted to compare robust and nominal solutions for various uncertainty levels to verify the superiority of the robust solution.

Owing to the increasing complexity of managing IT infrastructure caused by rapid technological advancements, organizations are transforming their datacenter management environments from on-premises to the cloud. Datacenters operating in the cloud environment have large amounts of IT infrastructure, such as servers, storage devices, and network equipment, and are operational on all days of the year, thus causing power overconsumption problems. However, efforts to reduce power consumption are not the first priority as datacenters seek stable operation to avoid violating their service level agreements. Therefore, a research model that reduces power consumption of the datacenter while enabling stable operation by utilizing virtual machine (VM) consolidation is proposed here. To obtain the optimal solution for the proposed VM consolidation model, an adaptive harmony search methodology is developed, which expends less effort to set the parameters of the model compared to existing harmony search methods. Comparative experiments were conducted to validate the accuracy and performance of the proposed model. As a result, Original harmony search (HS) showed better performance than the existing heuristic algorithm, and novel self-adaptive (NS)-HS showed the best result among Adaptive HS. In addition, when considering workload stability, it showed better results in terms of datacenters (DC) stability than otherwise.