Collaborative production and predictive maintenance scheduling for flexible flow shop with stochastic interruptions and monitoring data. (October 2022) - Xia, Tangbin; Ding, Yutong; Dong, Yifan; Chen, Zhen; Zheng, Meimei; Pan, Ershun; Xi, Lifeng
Abstract: Flexible flow shop problem (FFSP) has been extensively researched in recent years. However, machine deterioration and stochastic production interruptions noticeably affect the regular job process in practice, incurring great cost. It is significant to incorporate the condition-based predictive maintenance scheduling and the production modification for interruptions with traditional FFSP. Nevertheless, this issue is rarely investigated in the existing FFSP studies. To solve the FFSP with maintenance and stochastic interruptions (FFSP-MSI), this paper proposes a collaborative optimization policy of production and maintenance (COPPM) by considering stochastic interruptions and monitoring data utilization for total cost reduction. For maintenance scheduling, a real-time prognosis updating method is leveraged based on monitoring data to capture the individual machine degradations. Assisted by the prognosis output, the optimal maintenance intervals are dynamically derived through a cost rate model. For production optimization, a variable neighborhood search (VNS) algorithm is presented to obtain the nearly- optimal initial production plan and the production modification for stochastic interruptions caused by machine failures and random jobs. This proposed COPPM comprehensively determines the cost-effective maintenance cycle, job order and machine selection for each job. Computational experiments of the wind turbine blade process are presented to certify the economic effectiveness of COPPM. Compared with the traditional production and maintenance scheduling method, the COPPM achieves 13.68 % total cost reduction in average under various problem scales. Highlights: Condition-based maintenance scheduling and stochastic interruptions are considered in FFSP. Variable neighborhood search algorithm is used for production optimization. Massive condition monitoring data are utilized for dynamic predictive maintenance scheduling. Production modification is achieved in response to random jobs and unexpected machine failures. This policy can markedly reduce the total production, maintenance and tardiness cost.