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

• Published in: Computers & industrial engineering 2023-12, Vol.186, p.109708, Article 109708
• Main Authors: Hu, Chaoming, Zheng, Rui, Lu, Shaojun, Liu, Xinbao, Cheng, Hao
• Format: Article
• Language: English
• Subjects: Dynamic job arrivals; Mold constraints; Preventive maintenance; Production scheduling
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2023.109708

•Integrated optimization of production scheduling and maintenance planning.•Consideration of dynamic job arrivals and mold constraints for a single machine.•Development of an efficient hybrid differential evolution and genetic algorithm.•Development of a refinement strategy aimed at improving the solution quality produced by DE-GA.•The proposed method outperforms the other well known methods. 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.