Robust condition-based fleet operations and maintenance: Integrating multi-asset degradation rate interactions and operations-induced degradation

Effective operations and maintenance (O&M) in modern production and service systems hinges on a careful orchestration of economic and degradation dependencies across a fleet of assets. While the economic dependencies are well studied, incorporating degradation dependencies and their impact on system operations remain an open challenge. In this article, we consider a general operations & maintenance problem, where a fleet of assets are utilized to satisfy the demand. Assets undergoing maintenance are temporarily unavailable for production. The degradation rate is influenced by two primary decision-dependent factors. First, higher production rates lead to faster degradation of assets. Second, a degrading asset can accelerate the degradation of related assets in the system. To address these dependencies, we model condition-based production and maintenance decisions for multi-asset systems with degradation interactions. We provide the first O&M model to optimize O&M in multi-asset systems with embedded decision-dependent degradation interactions. We formulate robust optimization models that inherently capture degradation and failure risks by embedding degradation signals via a set of constraints, and building condition-based uncertainty sets to model probable degradation scenarios. Our approach offers a seamless integration of data-driven degradation modeling and mathematical programming to bridge the gap across predictive and prescriptive models. Performance of the proposed O&M model is evaluated through extensive experiments, where the degradation is represented using vibration-based readings from a rotating machinery system. The proposed model provides significant improvements in terms of operation, maintenance, and reliability metrics.