A novel maintenance optimization strategy for reconfigurable mass production system based on stochastic demand

Within the context of Industry 4.0, reconfigurable manufacturing systems (RMS) face critical reliability challenges when adapting to dynamic market demands. Traditional preventive maintenance (PM) strategies are often inadequate in effectively addressing the uncertainties introduced by frequent system reconfigurations, subsequently leading to unplanned downtime and increased costs. This paper proposes an adaptive maintenance timing strategy aimed at ensuring the reliability of reconfigurable mass production systems and reducing the incidence of failures. The strategy begins by analyzing, at each system reconfiguration point, the impact of the current system configuration and individual equipment preventive maintenance (PM) on production system downtime through an equipment-layer maintenance model. Subsequently, based on the minimum maintenance cost rate and an advance-postpone maintenance mechanism, the preventive maintenance timings for both fixed and reconfigurable equipment are determined separately, with the maintenance schedules for reconfigurable equipment being mapped onto the system timeline. Following this, depending on the assessed impact of equipment PM on system downtime, a decision is made to implement either combined or separated maintenance actions. Finally, based on the determined PM timings for all equipment at the system level, the total maintenance cost over the entire production planning period is calculated using a maintenance cost model. Case study results demonstrate that, compared to strategies solely employing either combined or separated maintenance, the proposed approach significantly reduces both maintenance time and costs. This provides an effective new strategy for RMS equipment maintenance and underscores its practical value in enhancing manufacturers’ ability to respond to dynamic market conditions.