Optimization of operations and maintenance (O&M) in the industry is a topic that has been largely studied in the literature. Many authors focused on reliability-based approaches to optimize O&M, but little attention has been given to study the influence of macroeconomic variables on the long-term maintenance policy. This work aims to optimize time-based maintenance (TBM) policy in the mining industry. The mine environment is reproduced employing a virtual model that resembles a digital twin (DT) of the system. The effect of maintenance decisions is replicated by a discrete event simulation (DES), whereas a model of the financial operability of the mine is realized through System Dynamics (SD). The simultaneous use of DES and the SD allows us to reproduce the environment with high-fidelity and to minimize the cost of O&M. The selected illustrative case example demonstrates that the proposed approach is feasible. The issues of using high dimensional simulation data from DT-models in managerial decision making is identified and discussed.

行业中的运营和维护（O＆M）的优化是一个在文献中进行了大量研究的主题。许多作者专注于基于可靠性的方法来优化运维，但很少关注研究宏观经济变量对长期维护政策的影响。这项工作旨在优化采矿业中基于时间的维护（TBM）策略。使用类似于系统的数字孪生（DT）的虚拟模型复制矿山环境。维护决策的效果可以通过离散事件模拟（DES）复制，而矿山的财务可操作性模型则可以通过系统动力学（SD）实现。DES和SD的同时使用使我们能够以高保真度重现环境并最大程度地降低运维成本。选择的示例性案例证明了该方法是可行的。识别和讨论了使用DT模型中的高维仿真数据进行管理决策的问题。