Abstract

Methodologies for designing maintenance policies, such as Reliability Centered Maintenance (RCM), provide a list of recommended preventive maintenance (PM) activities as well as the periodicity for their execution, to comply with safety and reliability standards. This information is then used to elaborate PM plans, defining the instants of execution of each PM activity in the policy throughout a planning horizon, while assuring adequate levels of equipment availability, labor and resource coordination, and spare parts management. Preferably, it is sought to group PM activities executions to form opportunistic work packages to reduce functionality interruptions on the equipment. To address the latter issue, this paper presents an optimization framework for the medium-term opportunistic planning of PM activities on a single machine. The proposed framework is formulated as a computationally tractable continuous time mixed-integer linear program (MILP) that determines the execution instants of PM activities, considering time window tolerances for advancing or delaying executions to maximize the number of grouped executions or, equivalently, minimizing the number of stoppages needed to comply with a PM policy. Diverse numerical experiments are conducted, considering variants of the model regarding the conservativeness in use of the time window tolerances and the inclusion of resource constraints for planning. The results show that the reductions on the number of stoppages of the PM plans, that can be achieved from different levels of time window tolerances, are more significant for PM policies composed by activities with more heterogeneous periodicities of execution.