A computationally inexpensive method to outsource facility maintenance services through the internet in real-time

Abstract

The paper introduces a real-time framework for selecting maintenance contractors to address building facility breakdowns promptly and cost-effectively. A faster and resource-efficient approach utilizing a Mixed Integer Linear Programming (MILP) method is proposed to achieve this. The method optimizes contractor selection by leveraging structured linked data and integrating maintenance into the Internet of Things (IoT) framework, reducing processing time. Implemented initially for elevator systems, this framework can be extended to other mechanical and electrical systems in buildings. It consists of three modules: the building's integrated data module, the application module, and the maintenance provider module. The first module integrates building databases through structured linked data, establishing a centralized information source. The second module employs mixed integer linear programming to select contractors based on cost, time, previous services quality score, and maintenance urgency. The third module facilitates communication between contractors and the building facility manager, enabling maintenance requests and offers. The MILP model considers budget and time constraints, as well as maintenance urgency to prioritize immediate maintenances. A case study demonstrates that this model offers a fast and effective information processing solution. The results indicate that the proposed framework significantly reduces decision-making time for facility managers, streamlining contractor selection. Furthermore, compared to conventional methods, this framework provides a more efficient and effective automated maintenance decision-making solution, thus making a valuable contribution to the field of building maintenance management.