Increasingly complex production environments pose new challenges and require the development of new methodological approaches in the search for effective and cost-efficient maintenance planning, taking into account their applicability in real industrial environments. In this context, this article proposes an extension of the integrated framework for opportunistic preventive maintenance (PM) planning, proposed originally in Viveros et al. integrating non-negligible execution times of PM activities and time-window tolerances criterion for the generation of opportunistic grouping schemes. This work offers the implementation of the proposed framework in a practical case within the Chilean mining industry, expanding the analysis on scarce resource availability scenarios for PM tasks to be performed on conveyor belts in the grinding process. The proposed planning optimization model is formulated under the mixed-integer linear programming (MILP) paradigm, and searches minimizing the asset unavailability under several tolerance levels for the case study analyzed. The results show a 35% downtime reduction for a maximum tolerance factor of 10% considering an unconstrained maintenance resource scenario, which confirms the ability of the model to increase asset availability, minimizing system interruptions, improving its cost efficiency, and enhancing productivity levels in the mining industry.

日益复杂的生产环境提出了新的挑战，需要开发新的方法论来寻找有效且具有成本效益的维护计划，同时考虑到它们在实际工业环境中的适用性。在这种情况下，本文提出了机会主义预防性维护 (PM) 规划的集成框架的扩展，最初由 Viveros 等人提出。整合 PM 活动的不可忽略的执行时间和用于生成机会性分组方案的时间窗口容限标准。这项工作在智利采矿业的一个实际案例中提供了所提议框架的实施，扩展了对在研磨过程中在传送带上执行的 PM 任务的稀缺资源可用性场景的分析。所提出的规划优化模型是在混合整数线性规划 (MILP) 范式下制定的，并在分析案例研究的几个容忍级别下搜索最小化资产不可用性。结果表明，考虑到不受约束的维护资源场景，在最大容差因子为 10% 的情况下，停机时间减少了 35%，这证实了该模型能够提高资产可用性、最大限度地减少系统中断、提高成本效率并提高生产力水平。矿业。