Comparing data-driven meta-heuristics for the bi-objective Component Repairing Problem

Due to both the increasing use of automation in production processes and the budget devoted for purchasing equipment, maintenance plays a key role in making a company competitive in the marketplace. Moreover, the use of data analysis techniques and the advent of Internet of Things make the IoT-based predictive maintenance possible. In addition, since all the resources (e.g., budget and human) involved in the maintenance activities are usually limited, a company is also interested in defining optimized maintenance plans. In this paper, the integration of IoT-based predictive maintenance with optimization techniques is investigated by developing a data-driven Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic aimed at efficiently defining maintenance plans. In particular, we address the bi-objective component repairing problem (b-CRP), aimed at determining the set of components of a production system to repair that are more likely to fail. Having the breakage probability of each component, derived from historical data, the system reliability is maximized and the maximum time required to repair one component among those selected in the solution is minimized, under constraints on both budget and time for performing the maintenance activities. Then, we compare the solutions of GRASP with those of an already existing bi-objective Large Neighborhood Search meta-heuristic.