Progressive Opportunistic Maintenance Policies for Service-Outsourcing Network With Prognostic Updating and Dynamical Optimization

Increasing machine investments and expensive operations and maintenance (O&M) costs have made manufacturing system leasing and maintenance service outsourcing gaining a momentum. Leading original equipment manufacturers, as lessors, have focused on providing cost-effective maintenance schemes to serve their client-enterprises (lessees) all over the world. However, individual equipment degradations, complex system structures, and global network layout bring challenges for the real-time decision-making. This article comprehensively develops a service-outsourcing progressive opportunistic maintenance methodology for a global service-outsourcing network with prognostic updating and dynamical optimization. At the equipment layer, an automated prognostic model is utilized to characterize and update the individual path of each leased equipment's degradation signals. At the local layer, an opportunistic maintenance policy is developed for balancing production capacity and optimizing maintenance decisions of each system with even series-parallel structure. At the global layer, a routing optimization policy is proposed for the service-outsourcing network by integrating service time windows and multiple geographical locations to optimize the service route of required maintenance teams and the service start time of each group set. Finally, this hierarchical methodology has been verified in a multilocation service-outsourcing network. Its mechanism with real-time prognostic updating and dynamical O&M optimization can significantly ensure cost reduction, service timeliness and network robustness.