Adaptive Preventive Maintenance for Flow Shop Scheduling With Resumable Processing

Abstract

In this article, we focus on a joint scheduling problem that considers the corrective maintenance (CM) due to unexpected breakdowns and the scheduled preventive maintenance (PM) in a generic $M$ -machine flow shop. The objective is to find the optimal job sequence and PM schedule such that the total of the tardiness cost, PM cost, and CM cost is minimized. Currently, most existing studies on the PM schedules are based on a fixed PM interval, which is rigid and may lead to poor performance, as the fixed strategy fails to effectively balance the trade-offs between the production scheduling and maintenance. To address this critical research issue, our novel idea is to dynamically update the PM interval based on the real-time machine age, such that the maintenance activity coordinates with the job scheduling to the maximum extent, which results in an overall cost saving. Specifically, a correction factor is introduced to dynamically update the PM interval and to help evaluate whether it is worthwhile to process the job first at the risk of the CM before performing the PM action. To demonstrate the effectiveness of the adaptive strategy, simulations and a case study on mining operations are conducted to show that the adaptive strategy outperforms the existing methods with a less total cost. Note to Practitioners —This article is motivated by the critical problem of balancing the trade-offs between production scheduling and maintenance in a flow shop production line, where jobs are processed on the machines in the same route. On the one hand, production scheduling aims to meet the customer demands on time. On the other hand, the maintenance actions help restore the machines’ reliability by reducing the machine failure rate. Most existing approaches to scheduling the PM are based on the fixed PM interval, which is rigid and may lead to poor performance. Although the nonperiodic PM interval strategy has been proposed in the literature for production scheduling, it still does not consider the adaptive PM strategy in the context of flow shop scheduling with multiple machines. To fill this literature gap, in this article, we first model a generic $M$ -machine flow shop considering both the CM and PM. Then, we suggest a new adaptive and easy-to-implement approach to dynamically update the PM interval and improve the decision-making on the PM schedules. Compared with the conventional maintenance policy, the adaptive one greatly reduces the total cost in real-time scheduling.