A two-stage integrating optimization of production scheduling, maintenance and quality

Zheng, Jiafei; Yang, Hongbing; Wu, Qingsong; Wang, Ziyang

doi:10.1177/0954405420921733

Cited by 13 publications

(6 citation statements)

References 35 publications

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“…Math Prog. ML [27] 2022 GA [28] 2021 BH, VNS [29] 2021 GA, MC [30] 2021 GA [31] 2021 GA [32] 2021 GrA [33] 2021 GA [34] 2021 CEA [35] 2021 GrA [36] 2021 GA [37] 2021 BD [38] 2021 RHA [12] 2021 MP NN [39] 2021 RL [40] 2021 RL [41] 2020 GA [42] 2020 MVO [43] 2020 TS [44] 2020 NSGA-II [45] 2020 ANSGA-III [46] 2020 MA MIP [47] 2020 GA MIP [48] 2020 BOMP [49] 2020 BB [50] 2020 RL [51] 2020 RL [52] 2020 NN [53] 2019 GA [54] 2019 OTA [55] 2019 AC [56] 2019 NSGA-II [57] 2019 BIP [58] 2019 MILP [59] 2019 MINLP [60] 2019 RL [61] 2019 RL [62] 2019 RL [10] 2018 GA [63] 2018 SA, GA [64] 2018 SA [65] 2017 NSGA-II [66] 2017 MA [67] 2017 MILP [68] 2017 RL [69] 2016 NSGA-II [70] 2016 LPT MILP…”

Section: Yearmentioning

confidence: 99%

Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines

Rodríguez

Kubler²,

Giorgio³

et al. 2022

Robotics and Computer-Integrated Manufacturing

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Section: Yearmentioning

confidence: 99%

Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines

Rodríguez

Kubler²,

Giorgio³

et al. 2022

Robotics and Computer-Integrated Manufacturing

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“…The job shop scheduling problem (JSSP) has been extensively studied in academia and industry. [1][2][3][4] Traditionally, it is assumed that all resources in production are deterministic in JSSP. However, various random disturbances will inevitably occur in actual manufacturing, 5,6 resulting in reduced productivity and delayed deliveries, etc.…”

Section: Introductionmentioning

confidence: 99%

“…(3) An online scheduling framework is developed to form the mapping relationship between the manufacturing environment and the optimal scheduling model. (4) Comparisons between IRDRL and other methods are carried out to prove the effectiveness and improvement of the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Dynamic scheduling for dual-objective job shop with machine breakdown by reinforcement learning

Gan

Zuo

Yang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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In modern complicated and changing manufacturing environments, unforeseen dynamic events such as machine breakdown or unexpected job arrival make required production resources unpredictable. The scheduling scheme is desired to maintain high stability in dynamic manufacturing environments. To cope with the classic disturbance of machine breakdown, a robust pro-active scheduling scheme is proposed by inserting the repair time into a disjunctive graph for reinforcement learning (IRDRL) in this paper. Firstly, a new mathematical model is developed to predict the machine fault which is assumed to be determined by service time and bearing load. Secondly, a disjunctive graph with breakdown information is designed to express the dynamic scheduling status. Then, an online scheduling framework is built based on the well-trained model through the proximal policy optimization (PPO) algorithm. Finally, compared with the classical methods such as the right-shift strategy and static model of reinforcement learning (RL), the proposed robust pro-active scheduling scheme is verified with high robustness, stability, and short running time.

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“…Preventive maintenance (PM) can improve the conditions of the machines and consequently improve the product quality and machine reliability. [2][3][4][5] However, PM activities consume resources and interrupt normal production, which may lead to considerable costs. Therefore, for MMSs, effective PM scheduling is required to ensure high product quality and machine reliability with the lowest possible PM cost.…”

Section: Introductionmentioning

confidence: 99%

A QMM-MOP methodology for the maintenance scheduling of multistage manufacturing systems with a stream of deterioration

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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In multistage manufacturing systems, a stream-of-deterioration (SOD) phenomenon poses two challenges for effective preventive-maintenance (PM) scheduling. First, the deterioration of each machine contributes to the deterioration of the final-product quality, and thus timely PM should be conducted to prevent excessive quality deterioration. Second, the deterioration of different machines leads to different degrees of deterioration in the final-product quality; thus, the PM of different machines will result in different degrees of improvement in the final-product quality. To address both challenges, a QMM-MOP methodology that adopts an interactive bi-level scheduling framework is proposed. In machine-level scheduling, a quality-integrated maintenance model (QMM) is developed by incorporating intermediate-product quality deterioration into the total cost to schedule timely PM for each individual machine. In system-level scheduling, maintenance-operation prioritization (MOP), based on a SOD-enabled quality-improvement factor, is proposed to select machines for PM. The case study shows that the proposed methodology can ensure a higher final-product quality with a lower total cost. The contribution of this paper is to develop a QMM-MOP methodology that integrates product-quality improvement into an interactive bi-level PM scheduling framework and enables MOP based on the-quality improvement factor to best improve the final-product quality.

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A two-stage integrating optimization of production scheduling, maintenance and quality

Cited by 13 publications

References 35 publications

Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines

Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines

Dynamic scheduling for dual-objective job shop with machine breakdown by reinforcement learning

A QMM-MOP methodology for the maintenance scheduling of multistage manufacturing systems with a stream of deterioration

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