Functional resonance analysis via a genetic algorithm to ensure cost-effective maintenance planning

The tight interaction between organizations, humans and technologies drives production systems towards an ever-increasing complexity. Novel methods are required to support complex managerial actions and ensure their cost effectiveness. In this context, the objective of this research is to propose a methodology to optimize the maintenance operations of safety-critical production and service systems. The methodology is grounded on the principles of the Functional Resonance Analysis Method (FRAM), that has proved to be an effective approach to study complex socio-technical orchestrations. The FRAM is then integrated with a genetic algorithm (GA) to quantify functional performance indicators capable of optimizing maintenance practices, via an economically advantageous tasks allocation. The methodology is instantiated on a real case study related to maintenance operations of aircraft. Promising results have been obtained in the proposed application: a saving of almost 6% (128 man-hours) in resource allocation over 16 days of work has been highlighted for the real case study at hand. The methodological result provides maintenance planners with a support tool to guide decision making, ensuring cost effectiveness of operations, and emphasizing the direct relationships with economical convenience. Overall, the integration of the FRAM with GA proposed in this paper presents a strategy to address cost-effective maintenance planning in complex systems.