An adaptive maintenance decision methodology integrating multiagent-based modelling and simulation with a multistage evolutionary game model

Dynamic interactions among multiple agents in maintenance scenarios are common. Flexible maintenance strategies can enhance system reliability, reduce downtime, and minimize operational costs to achieve system health management in long-term operations. Therefore, investigating the complexity and interdependence of these agents in performing maintenance operations from the perspective of system maintenance decision-making is beneficial. In this paper, we propose the integration of multiagent-based modelling and simulation (MABMS) with a multistage evolutionary game (EG) model for the development of adaptive maintenance strategies. In the proposed method, MABMS is applied to describe the interactions among various agents. Stakeholders related to agents in MABMS are regarded as players in a game. Game theory can thus be adopted to model the strategic decision-making of stakeholders at different maintenance stages. We subsequently established a multistage EG to study the strategies of both competition and cooperation among agent-related stakeholders. Stakeholders at different stages optimize their strategies on the basis of feedback from agents and the results of relevant maintenance stages. Finally, by improving decision-making across different maintenance stages, a dynamic maintenance strategy is established to enhance system reliability and reduce downtime. The obtained results indicate that the proposed approach yields improvements in maintenance efficiency and decision-making adaptability.