Joint optimization of maintenance and inventory policies for multi-unit systems

Abstract

A maintenance policy and the management of the inventory of spare parts and their joint optimization often challenge managers and researchers. In this paper, the first analytical joint optimization model is established. A simulation model is then developed for the system operating under the suggested condition-based maintenance to optimize the maintenance outline of mining dump truck motors based on oil monitoring. Our model is combined with a genetic algorithm to obtain the optimal response. In the presented model, the Inspection intervals ( $$T$$ T ) and the maximum stock level ( $$S$$ S ) are jointly optimized for minimizing cost. To build a sample and a simulation of various repair events, 11,000 oil analysis data is used. The deterioration of spare parts is shown with an increasing numerical variable over time, which follows a function. Using the existing datasets, the deterioration rate function is obtained. Another process required for simulation is the failure probability function. Due to the extent of deterioration with various breakdowns, there are uncertainties and different values. Condition-based maintenance is used to determine the deterioration level of failure. In the end, the results of the simulation are compared with the current costs resulting from the workshop policies.