Imperfect preventive maintenance optimization for flexible flowshop manufacturing cells considering sequence-dependent group scheduling

Flexible flowshop manufacturing cells (FFMCs) have been widely applied to achieve the efficiency of high-volume manufacturing for products of small-to-medium demand. In FFMCs, jobs from several groups are processed at several stages with the same route and group-varying machining conditions. This paper integrates imperfect preventive maintenance (PM) and sequence-dependent group scheduling (GS) in FFMCs. A machine-level model is developed to describe machine reliability evolution under group-varying conditions. A system-level model is proposed to simultaneously obtain the planning of PM and GS by minimizing PM cost, minimal repair cost, and job tardiness cost in FFMCs. To solve the model, a simulated annealing embedded genetic algorithm (SAGA) is developed. Experiments show that the PM policy obtained by our proposed model can remarkably save cost when compared with three other types of policies. Further experiments present that the integer-key based chromosome representation of SAGA excels a random-key based representation, and SAGA outperforms SA and GA in terms of both the solution quality and robustness.