Integrated production planning and preventive maintenance scheduling for synchronized parallel machines

Production synchronization is an emerging paradigm in advanced manufacturing systems, with which the inventory space and cost of finished products can be significantly reduced. This paradigm has been studied in the past years, and several mathematical models have been developed to evaluate production synchronization for a diversity of manufacturing systems. Most of the existing studies on production synchronization, however, assume that machines are always functioning without malfunctions in production process. It is no doubt that machine failures can definitely lead to unexpected breakdowns that may produce non-ignorable impact on production synchronization. In this study, a new evaluation model for production synchronization is put forth for parallel machines. The production time uncertainty caused by unexpected breakdowns of machines is explicitly quantified, and the distribution of the job completion time on a machine is approximated by the Gamma distribution through the moment match method. By integrating production planning and preventive maintenance scheduling together, a new joint optimization problem is formulated for synchronized parallel machines, and it is resolved by a modified cooperative co-evolutionary genetic algorithm with a heuristic initialization. Two illustrative examples along with a set of comparative studies are presented to examine the effectiveness of the proposed model and solution algorithm.