Development and Optimization of maintenance Using Monte Carlo Method

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Abstract Global competition has led to extraordinary changes in the way firms function. These developments have influenced maintenance and made its function even more vital to corporate success. To stay competitive, organizations must continuously enhance the amount of maintenance,so considerable efforts have been devoted in improving the economic performance of maintenance strategies for stochastically degrading production systems.This study is a contribution to robust decision making in maintenance of systems vulnerable to slow deterioration. Our first contribution is to create a criteria permitting the combined assessment of the mean economic performance and the resilience of various kinds of maintenance techniques. The benefit of the suggested criteria is that it adapts to various kinds of maintenance techniques and offers access to a simple and relevant assessment model. Specifically, using the long-term expected maintenance cost rate as the performance and the variance of maintenance cost per renewal cycle as the robustness, the study examines three representatives of time-based (BTM) and condition-based maintenance (CBM) families: a block replacement strategy (BR), a periodic inspection and replacement strategy (PIR) and quantile based and replacement strategy (QIR) . Mathematical cost models are created based on the homogeneous Gamma degradation process and probability theory. By using the Monte Carlo Method, the assessment of maintenance techniques involves a comparison against each other, applying a unique criteria to quantify performance and robustness in decision-making for each modification.

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