ABSTRACT
Optimization of Human Resources (HR) and re-adaptation are of vital importance to enterprises in order to keep the workload balanced and maintain high performance levels when unexpected events or exceptions occur, such as an arrival of a new unscheduled task. In this paper, a novel HR optimization & re-adaptation model for enterprises is introduced. The model integrates different entities such as employees, processes, work schedules, resources, and location information. The heterogeneous information is translated to a common vocabulary in order to be utilized for assigning tasks to human resources automatically without the need of supervision. Conditional Random Fields (CRFs) probabilistic models are trained, so as to learn the already applied task assignment patterns, and their output is taken into account in the decision process. The HR optimization toolkit, which comprises the models and the HR optimization tool, has been tested with real data acquired from an industrial environment achieving favourable results towards HR assignment.
