A simulation-based optimization approach evaluating maintenance and spare parts demand interaction effects

Abstract

Industrial facilities frequently experience significant production losses due to unanticipated failures, sub-optimal maintenance, operational and spare parts logistics challenges. These among other factors directly affect the plant's performance measures such as availability, repair time and costs. Consequently, optimization addresses such challenges. However, a fundamental problem presented here relates to the need for a framework that assists in the determination of critical system to be optimized, variables that significantly impact the performance of such systems, and subsequently undertake optimization. To realistically model such complexities, a framework that applies the discrete simulation model of critical repairable subsystems, undergoing deterioration is proposed. The study utilises empirical maintenance data, where Pareto analysis is employed to identify critical subsystems, while expert input is incorporated to derive model variables. A full factorial Design of Experiment (DOE), is employed to establish the variables with significant main and interaction effects on the total repair time and subsequently employed as decision variables for a simulation-based optimization. The proposed framework is demonstrated in a case study of a thermal power plant. Simulation results highlight the turbocharger as the critical subsystem, while spares availability, the time between overhaul (TBO) and reliance on different maintenance strategies exhibit most significant main and interaction effects. The optimization results obtained demonstrate that TBO, spares availability and reliance on various maintenance strategies, provide a significant impact on the reduction of the repair time. The framework enhances maintenance decision making by optimizing the plants' operational and maintenance related factors identified.

Towards an innovative lubricant condition monitoring strategy for maintenance of ageing multi-unit systems.

“…𝐹𝑆 1 represents minor,𝐹𝑆 2 moderate,𝐹𝑆 3 , severe and 𝐹𝑆 4 , an extensive stop in production/operation. The transition of the unit's degradation level (depicting stochastic imperfect maintenance [31]), depends on the prior level, the maintenance intervention and follows the Semi-Markov Decision Process as described in [32]. Fig 2.…”

“…The transition of the degradation levels for both the iron and viscosity properties are similarly dependent on the prior level and the lubricant related intervention 𝑅 𝐿 𝑖 employed. Table 2 illustrates the different severities 𝐹𝑆𝑂 𝑗 (viscosity) and 𝐹𝑆𝐼 𝑗 (iron) transition matrix that assumes a Semi-Markov Decision Process also employed by [32].…”

Availability Analysis of the Critical Production System in SMEs Using the Markov Decision Model

“…ey have implemented and proposed the optimal solution to the existing single vacant taxi routing problem using MDP in the cab travel agency as a result of their research [12]. e most critical components (turbocharger, cylinder, and governor) of the thermal power plant were investigated and analyzed using the time between overhaul approach and SMDP to propose the suitable and optimal decision-making process for maintenance in the thermal power plant [13].…”

A System Dynamics Model for Evaluating the Firms’ Capabilities in Maintenance Outsourcing and Analyzing the Profitability of Outsourcing

“…Regarding simulation and maintenance, the simulation-based optimization algorithm has been used in many studies, including studies by Irawan et al [28], Wakiru et al [29], Horvath et al [30], Packianather et al [31], and Ganji et al [32]. Moreover, Qin et al [33] proposed a two-step optimization approach for aircraft hangar maintenance planning and staffing problems under outsourcing.…”