Prognostic and Health Management (PHM) benefits are strongly tied to the decision-making that follows the assimilation and interpretation of prognostics information. Hence, we deal in
this study with the post prognostic decision making in order to improve system safety and avoid downtime and inopportune maintenance spending. We investigate the problem of scheduling production jobs in a single multifunctional machine subjected to predictive maintenance based on PHM results. For this reason, we propose a new interpretation of PHM outputs to define the machine degradation corresponding to each job. We develop a Mixed Integer Linear Programming (MILP) model to find the best integrated scheduling that optimizes the total maintenance cost. Unfortunately, the MILP is not able to compute the optimal solution for large instances. Therefore, we design a Prognostic based Genetic Algorithm (Pro-GA). Computational results of different benchmarks setup show the efficiency and robustness of our scheme with an average deviation of about 0.2% over a newly proposed lower bound.
Maintenance Scheduling, predictive maintenance, degradation, Post-prognostics decision, Genetic Algorithm optimization, Mixed Integer Linear Programming
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