Abstract

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.