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Scheduling the maintenance based on the condition, respectively thedegradation level of the system leads to improved system's reliability whileminimizing the maintenance cost. Since the degradation level changesdynamically during the system's operation, we face a dynamic maintenancescheduling problem. In this paper, we address the dynamic maintenancescheduling of manufacturing systems based on their degradation level. Themanufacturing system consists of several units with a defined capacity and anindividual dynamic degradation model, seeking to optimize their reward. Theunits sell their production capacity, while maintaining the systems based onthe degradation state to prevent failures. The manufacturing units are jointlyresponsible for fulfilling the demand of the system. This induces a couplingconstraint among the agents. Hence, we face a large-scale mixed-integer dynamicmaintenance scheduling problem. In order to handle the dynamic model of thesystem and large-scale optimization, we propose a distributed algorithm usingmodel predictive control (MPC) and Benders decomposition method. In theproposed algorithm, first, the master problem obtains the maintenancescheduling for all the agents, and then based on this data, the agents obtaintheir optimal production using the distributed MPC method which employs thedual decomposition approach to tackle the coupling constraints among theagents. The effectiveness of the proposed method is investigated on a casestudy.

Distributed joint dynamic maintenance and production scheduling in  manufacturing systems: Framework based on model predictive control and  Benders decomposition