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 (\(T\)) and the maximum stock level (\(S\)) 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.

维护政策，备件库存管理以及联合优化经常会给管理人员和研究人员带来挑战。本文建立了第一个解析联合优化模型。然后为在建议的基于条件的维护下运行的系统开发一个仿真模型，以基于油的监控来优化矿用自卸车电机的维护大纲。我们的模型与遗传算法结合以获得最佳响应。在提出的模型中，检验间隔（\（T \））和最大库存水平（\（S \））进行了联合优化，以最大程度地降低成本。为了构建样本并模拟各种维修事件，使用了11,000个油分析数据。随着时间的流逝，随着时间的推移，随着数值的增加，备件的劣化也随之增加。使用现有的数据集，可获得劣化率函数。模拟所需的另一个过程是故障概率函数。由于各种故障的恶化程度，存在不确定性和不同的值。基于状态的维护用于确定故障的恶化程度。最后，将模拟结果与研讨会政策产生的当前成本进行比较。