A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops

This paper deals with the integration of production scheduling and maintenance planning in order to optimize the bi-objective of quality robustness and solution robustness for flow shops with failure uncertainty. First, a proactive model is proposed to formulate the problem mathematically. Then, Monte Carlo sampling method is adopted to obtain the objective value for feasible solutions and a surrogate measure is proposed to approximate the objective function efficiently. Based on the sampling method and surrogate measure, a two-loop algorithm is devised to optimize the sequence of jobs, positions of preventive maintenances and idle times simultaneously. Computational results indicate that solution robustness and stability of quality robustness can be significantly improved using our algorithm compared with the solutions obtained by the traditional way.