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Improved Genetic Algorithm for the Fuzzy Flowshop Scheduling Problem with Predictive Maintenance Planning

This study proposes an improved genetic algorithm for the fuzzy permutation flowshop scheduling problem under availability constraints with makespan criterion. Machines unavailabilities are due to predictive maintenance interventions scheduled based on Prognostics and Health Management (PHM) results. Moreover, to take into account the several sources of uncertainty in the prognosis process, we model PHM outputs using fuzzy logic. The proposed genetic algorithm was calibrated based on sensitive statistical analysis. Computational experiments carried out on Taillard well known benchmark sets for permutation flowshop to which we add both PHM and maintenance data, show that the proposed algorithm seems to be efficient and effective.