Preventive Maintenance and Job Shop Scheduling: An Approach to React to Unplanned Events and Minimizing Unused Time Slots in Production Control

The research develops a strategy for order-based production of complex products in single item and small batches, characterized by a heterogeneous machine park. It addresses key Industry 4.0 challenges such as unplanned events and production downtime, exacerbated by tight deadlines and product variety. Developing solutions to these complexities is critical, and integrating maintenance into production control tasks offers high innovation potential. The primary aim of this research is to develop an approach to integrate preventive maintenance strategies into the production control of complex environments. The methods include a systematic literature review to identify gaps in existing research, followed by the development of a mixed-integer programming model tailored to job shop scheduling. This model optimizes the allocation of production orders and preventive maintenance tasks by dynamically adjusting to unplanned events. The approach is illustrated using a fictitious scenario and shows the potential for optimizing capacity utilization and machine reliability. Supported by a customized job shop scheduling algorithm, this approach makes production and maintenance processes more flexible, enabling rapid responses to unplanned events and optimizing unused time slots, particularly for maintenance on critical machines. The paper describes a job shop model integrating preventive maintenance strategies and presents initial results: a new machine assignment plan following unplanned events. This integration reduces machine downtime and makes more effective use of available capacity by coordinating maintenance and production tasks. This approach optimizes machine utilization and allows the production plan to be adapted quickly, which is particularly important for production and maintenance planners in a highly dynamic environment.