Mission reliability-oriented integrated scheduling optimization model by combining production planning and maintenance activities. (April 2021)

• Record Type: Journal Article
• Authors: Gu, Changchao; He, Yihai; Chen, Zhaoxiang; Han, Xiao; Zhou, Di; Zhang, Anqi
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 5(2021)
• Page Start: 839
• Page End: 849
• Publication Date: 2021-04
• Subjects: Manufacturing system -- integrated scheduling optimization model -- production planning -- maintenance activities -- mission reliability; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405420971080
• Languages: English
• ISSNs: 0954-4054

Abstract

Machine utilization and production efficiency of manufacturing systems can be effectively improved through reasonable production scheduling. Traditionally, production scheduling and maintenance planning are considered as two independent issues, but it may lead to a suboptimal solution that is unable to maximize the productivity of the manufacturing system. Therefore, a mission reliability-oriented integrated scheduling model that considers production planning and maintenance activities is proposed. Firstly, the mission reliability that takes into account product type and equipment performance is defined to characterize production rhythm. Secondly, the maintenance strategy based on machine degradation cumulative failure and stochastic failure is proposed to guarantee the mission reliability of the machine effectively. Thirdly, an integrated scheduling model is established with the goal of minimizing total operational time, and Genetic algorithm is tailored to find the best production scheduling plan. Finally, a case study and comparative study of the cylinder head manufacturing system are presented to demonstrate the effectiveness of the proposed method. Results show that the proposed method is more suitable for production practice than the previous production scheduling strategy.