%0 Journal Article %T Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control %+ Shaanxi University of Science and Technology %+ School of Automation and Electrical Engineering [Beijing] (University of Science and Technology Beijing) %+ Signal Processing Laboratory (SPL) %+ Laboratorio CINI-Item "Carlo Savy" %+ Laboratoire Modélisation et Sûreté des Systèmes (LM2S) %+ Beihang University (BUAA) %A Wang, Tian %A Cheng, Jiaxiang %A Yang, Yi %A Esposito, Christian %A Snoussi, Hichem %A Tao, Fei %< avec comité de lecture %@ 1545-5955 %J IEEE Transactions on Automation Science and Engineering %I Institute of Electrical and Electronics Engineers %P 1-9 %8 2020-12-22 %D 2020 %R 10.1109/TASE.2020.3043393 %Z Engineering Sciences [physics]/Signal and Image processingJournal articles %X The automated conveyor system, as the core component in the modern manufacturing world, has gained lots of attention from researchers. To optimize the operation of the conveyor system, range-inspection control (RIC) has been considered an efficient strategy to bring this conventional technology to an intelligent level. Various algorithms have been put into use to achieve optimal control. However, the current methodologies are only focusing on control optimization, not scaled into the smart manufacturing framework. The schema of alignment and corporation between the physical and virtual spaces for the system remains an important problem. Therefore, the work in this article aims for an effective framework of implementation between the physical and virtual stations in an automated conveyor system. Since increasingly more application scenarios rely on the digital twin (DT) technology to realize the integration of physical and virtual systems, we proposed the DT automated conveyor system (DT-ACS) that constructs the road map to implement the RIC-based conveyor system under the background of a smart factory. Besides, profit-sharing-based deep Q-networks (PDQNs) have been proposed to cope with the RIC optimization problem. The robustness and efficiency of the proposed PDQN were evaluated via sets of experiments. The discussion and conclusion are presented at last accordingly. %G English %L hal-03320628 %U https://utt.hal.science/hal-03320628 %~ CNRS %~ UNIV-TROYES %~ UTT %~ UTT-LIST3N %~ LM2S-UTT