Abstract

This research presents the elaboration and computational implementation of a framework for optimizing planning strategies on maintenance interventions. Our model comprehends a novel integrated approach for the opportunistic grouping strategy of preventive maintenance activities originally presented in Viveros et al. (2020), incorporating through this extension new criteria to improve applicability in real industrial environments, i.e., a technical feasibility criterion for grouping, whereas a non-negligible repair time for preventive maintenance activities, and the application of time-window tolerances in order to facilitate opportunistic maintenance grouping schemes. This work also develops an optimization model based on the mixed-integer linear programming (MILP) paradigm for the implementation of the present framework. Our numerical experiments show a 39% downtime reduction in the system under analysis, considering a maximum tolerance factor of 10% for six preventive maintenance activities, demonstrating the framework’s effectiveness to improve productivity and reduce fixed maintenance costs. This research aims to formulate a new proposal for efficient maintenance planning, which considers realistic applicability criteria to facilitate the transfer of knowledge and its industrial application, with an approach oriented to the simulation and risk quantification of failure events in complex systems.