Automated staff assignment for building maintenance using natural language processing

Staff assignment is the decision-making to determine appropriate workforce with required skills to perform a specific task. Staff assignment is critical to success of construction projects, especially when responding to routine requests such as the change order and building service. However, the effectiveness is low due to manual processing by the management personnel. To improve the productivity of staff assignment, this paper creates a machine learning model that reads service request texts and automatically assigns workforce and priority through the technique of natural language processing (NLP). The dataset used for modeling in this study contains 82,106 building maintenance records for a 3-year period from over 60 buildings on a university campus. The results show a 77% accuracy for predicting workforce and an 88% accuracy for predicting priority, indicating a considerably high performance for multiclass and binary classifications. Different from existing studies, the NLP model highlights the value of stop-words and punctuation in learning service request texts. The NLP model presented in this study provides a solution for staff assignment and offers a piece of the puzzle to the information system automation in the construction industry. This study has an immediate implication for building maintenance; and, in the long term, contributes to human-building interactions in smart buildings by connecting human feedback to building control systems.