Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints. (June 2022)

• Record Type: Journal Article
• Authors: He, Xinxin; Wang, Zhijian; Li, Yanfeng; Khazhina, Svetlana; Du, Wenhua; Wang, Junyuan; Wang, Wenzhao
• Is Part Of: Reliability engineering & system safety. Volume 222(2022)
• Publication Date: 2022-06
• Subjects: Gated recurrent unit -- Job-machine release times -- Makespan -- Parallel machine scheduling -- Preventive maintenance -- Remaining useful life prediction -- Teaching and learning based optimization; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108429
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A MILP model for parallel machine scheduling and maintenance is proposed. Job-machine release and maintenance duration/interval flexibility are considered. A RUL prediction method is proposed to get online machine reliability. Online machine reliability is used to guide all maintenance activities. Abstract: The machine remaining useful life (RUL), the job-machine release time and the correlation between the maintenance duration and the machine enlistment age are, in this paper, collectively emphasized at the parallel machine scheduling problem. Based on this, a corresponding mixed integer programming model is constructed to minimize the makespan and the processing loss beyond the machine RUL threshold, where a discrete teaching and learning based optimization algorithm is applied to solve this NP-hard problem, and a fault mode-assisted gated recurrent unit (FGRU) life prediction method is used to guide the predictive maintenance initiation time of all machines. In addition, this paper demonstrates that the FGRU method is more accurate than three common methods (Encoder-Decoder Recurrent Neural Network, Bidirectional Long Short-Term Memory and GRU) through two actual bearing degradation cases, and shows through three benchmark cases that the joint decision-making can effectively reduce the time cost of manufacturing enterprises.