ABSTRACT Human Resource Allocation (HRA) can be defined as the way professionals are distributed across the organization’s tasks, given that each individual has his/her own set of characteristics, and that each task has specific needs. Thus, this paper puts forward a mathematical programming model for allocating human resources that considers employees’ formal qualifications and experience and the possibility of employees sharing tasks in each project. The proposed mathematical model was designed and implemented according to a set of heuristics based on a Greedy Search (GS), a Genetic Algorithm, a Cosine Pigeon- Inspired Optimizer and an Iterated Local Search (ILS), to solve small, medium and large random instances. Thus, it was verified which of the heuristics had the best performance according to certain indicators, such as resolution time and average quality of the solutions found. Finally, they were also compared with the optimal solution obtained for small and medium-sized instances, with the best average results to ILS, although these are not too far from those of the GS.

ABSTRACT The direction of public policies plays an important role in society as a whole, especially in security, which, in addition to being considered a necessity for every citizen, is constitutionally guaranteed. This study presents the use of an unsupervised learning approach for the establishment of clusters among the municipalities in the State of Pernambuco, Brazil, considering some types of representative crimes, aiming to direct actions to prevent and fight crime in order to support policy makers. The k-means algorithm was used as the main tool in the study, using the software R 3.6.1, and recommendations for actions were directed to each of the obtained clusters. To demonstrate the direction, the grouping with the parameter k = 26 was used, referring to the State Security Integration Areas. The results show that the use of a clustering approach for the municipalities provides greater effectiveness in directing actions to combat and prevent crime, given that the municipalities that have the greatest similarities are grouped in the same cluster.