Using a Genetic Algorithm, Integrated Preventive Maintenance Planning and Production Scheduling for a Single Machine

Although preventive maintenance and production scheduling are closely related, most industrial units plan them independently. Preventive maintenance is typically scheduled to maximize machine availability, whereas production scheduling aims to optimize customer satisfaction, often measured by minimizing total weighted expected completion time. This study addresses this gap by proposing integrated and realistic approaches to merge production scheduling and maintenance planning models. The proposed framework is applied to Rafsanjan Arvand Wheel Co. to evaluate its practicality and effectiveness on an industrial scale. Implementation results demonstrate not only the successful synchronization of production and maintenance activities but also substantial performance improvements. Specifically, the integrated strategy leads to a ۴۲% reduction in overall weighted completion time of client orders, significantly enhancing customer satisfaction. Detailed analyses highlight how aligning maintenance and production planning can optimize resource utilization, reduce operational conflicts, and improve service levels simultaneously. These findings suggest that industrial firms can achieve both operational efficiency and improved customer outcomes by adopting integrated scheduling approaches. The proposed model provides a robust decision-support tool for managers seeking to optimize complex production-maintenance interactions, offering a practical pathway toward more efficient and customer-focused industrial operations.

Keywords:

Genetic Algorithm , Preventive maintenance planning , Production scheduling , optimization