Joint optimization of preventive maintenance and flexible flowshop sequence-dependent group scheduling considering multiple setups

ABSTRACT This study focuses on a joint optimization problem regarding preventive maintenance (PM) and non-permutation group scheduling for a flexible flowshop manufacturing cell in order to minimize makespan. A mixed-integer linear programming model for the investigated problem is developed, which features the consideration of multiple setups, the relaxation of group technology assumptions, and the integration of group scheduling and PM. Based on the model, a lower bounding technique is presented to evaluate the quality of solutions. Furthermore, a genetic algorithm (GA) is proposed to improve computational efficiency. In the GA, a threshold-oriented PM policy, a hybrid crossover and a group swap mutation operator are applied. Numerical experiments are conducted on 45 test problems with various scales. The results show that the proposed model can remarkably reduce makespan. Comparative experiments reveal that the GA outperforms CPLEX, particle swarm optimization and cuckoo search with respect to effectiveness and efficiency.