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.
Finding optimal maintenance policies for complex multi-component systems is a realworld challenge in the industry. This paper compares three maintenance policies for complex systems with non-identical components and economic dependencies in case of fault. Discrete event and Monte Carlo simulation are used to replicate fault occurrences, while a genetic algorithm is used to minimize the cost of maintenance by finding optimal groups of maintenance activities. Low total average maintenance cost and high average availability of the system are considered as desirable objectives and the capacity of the studied policies to achieve these goals is analyzed. None of the policies dominates the others (in a Pareto efficiency sense), thus making the policy choice context dependent and subject to decision makers' preferences.
During the last decade, the manufacturing industry has gone through a deep transformation with the digitalization of processes, the arrival of the Internet of Things, the spread of artificial intelligence (AI) in daily practices, and the ubiquitous presence of data-thanks to the cloud technologies lifting the efficiency of manufacturing systems to a new level. Notwithstanding these radical changes, the manufacturing industry still has a strong dependence on maintenance, a field that is still considered to be a necessary evil by most managers, but without which plants and equipment will not remain safe and reliable. The importance of
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