An adaptive CGAN/IRF-based rescheduling strategy for aircraft parts remanufacturing system under dynamic environment

• Published in: Robotics and computer-integrated manufacturing 2019-08, Vol.58, p.230-238
• Main Authors: Zheng, Peng, Wang, Junliang, Zhang, Jie, Yang, Changqi, Jin, Yongqiao
• Format: Article
• Language: English
• Subjects: Adaptive systems; Aircraft; Aircraft components; Aircraft control; Aircraft parts; Aircraft parts remanufacturing system; Dynamic environment; Improved random forest; Optimization; Production planning; Remanufacturing; Rescheduling; Reusable components; Schedules
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2019.02.008

•A data-driven adaptive rescheduling strategy is proposed.•Hybrid triggering mechanism to response frequent gradual disruptions.•Conditional generative adversarial networks for class-imbalanced data set.•Improved random forest as a classifier for rescheduling method. The production planning and control of aircraft remanufacturing system is becoming a critical issue with the development and application of reusable aircraft parts. With the unpredictable demands of remanufactured products, the aircraft parts remanufacturing system (APRS) operates with various disruptions, which could seriously influence the system's efficiency. To generate timely and efficient response towards the frequent disruptions in APRS, an adaptive rescheduling policy is presented in this paper with a trigger and a re-scheduler. The trigger evaluates the system performance loss caused by the disruptions with a specific Relative Performance Deviation Index (RPDI) and determines when the re-scheduler is activated to re-optimize the schedule. The re-scheduler then intelligently selects the optimal rescheduling method by an improved random forest (IRF) based on the system status described by a set of reduced but important features. In order to deal with the data imbalance in different classes in the remanufacturing system, this paper develops a problem-oriented conditional generative adversarial networks (CGAN) for data augmentation. Aiming to demonstrate the effectiveness of the proposed method, an experiment is conducted with three commonly used classification approaches. And, the results show the competitiveness of the proposed approach in reducing the rescheduling trigger times and the rescheduling performance improvement.