LAUSR

Abstract Two-machine flow shops are widely adopted in manufacturing systems. To minimize the makespan of a sequence of jobs, joint optimization of job scheduling and preventive maintenance (PM) planning has been extensively studied for such systems. In practice, the operating condition (OC) of the two machines usually varies from one job to another because of different processing covariates, which directly affects the machines’ failure rates, PM plans, and expected job completion times. This fact is common in many real systems, but it is often overlooked in the related literature. In this study, we propose a joint decision-making strategy for a two-machine flow shop with resumable jobs. The objective is to minimize the expected makespan by taking into account job-dependent OC. We consider two situations. In the first situation, where the failure rate of a machine under a fixed OC is constant, a hybrid processing time model is proposed to obtain the optimal job sequence based on the Johnson's law. For the second situation, where the failure rate of a machine is time-varying, the job sequence and PM plan are jointly optimized. An enumeration method is adopted to find the optimal job sequence and PM plan for a small-scale problem, and a genetic algorithm-based method is proposed to solve a large-scale problem. Numerical examples are provided to demonstrate the necessity of considering the effect of job-dependent OC and the effectiveness of the proposed method in handing such joint decision-making problems in manufacturing systems.