Integrated optimization of production scheduling and maintenance planning with dynamic job arrivals and mold constraints

Effective management of production often requires harmonizing maintenance and production activities. However, decsions on production scheduling and machine maintenance are usually made independently, leading to potential overlaps and inefficiencies. This paper joinly investigates the production scheduling and maintenance planning for a production machine subject to different types of jobs that arrive randomly. A unique emphasis is placed on the prerequisite of loading specific molds prior to job execution, an element often overlooked in previous studies. Maintenance considerations employ the reliability/availability framework. The overarching goal is the creation of an integrated schedule that minimizes the weighted sum of total costs and the machine's maximum unavailability. To address this intricate challenge, a novel hybrid differential evolution and genetic algorithm (DE-GA) is proposed, complemented by a solution refinement strategy. Performance evaluations indicate that DE-GA methodology consistently outperforms Gurobi solver and four other prevalent algorithms across different test instances.