Browsing by Author "Liu, Yang"

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    An available-to-promise stochastic model for order promising based on dynamic resource reservation policy
    (Taylor & Francis, 2022-07-29) Qin, Wei; Zhuang, Zilong; Sun, Yanning; Liu, Yang; Yang, Miying
    Facing uncertain future customer orders, a pull-based available-to-promise (ATP) mechanism will deteriorate the overall profit since it allocates critical resources only to current customer orders. To prevent current less-profitable customer orders from over-consuming critical resources, this study investigates a push–pull based ATP problem with two time stages and three profit margin levels, and develops a dynamic resource reservation policy to maximise the expected total profit. Then, a corresponding push–pull based stochastic ATP model is established with known independent demand distributions, and the optimal reservation level is derived by the genetic algorithm to maximise the expected total profit. Finally, a series of simulation experiments are conducted to reveal the impact of some key factors, and the experiment results provide theoretical guidance and implementation methods for companies to maximise overall profits.
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    Reinforcement learning based optimal decision making towards product lifecycle sustainability
    (Taylor & Francis, 2022-01-31) Liu, Yang; Yang, Miying; Guo, Zhengang
    Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact.
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    Reprogrammed tracrRNAs enable repurposing of RNAs as crRNAs and sequence-specific RNA biosensors
    (Nature Publishing Group, 2022-04-11) Liu, Yang; Pinto, Filipe; Wan, Xinyi; Yang, Zhugen; Peng, Shuguang; Li, Mengxi; Cooper, Jonathan M.; Xie, Zhen; French, Christopher E.; Wang, Baojun
    In type II CRISPR systems, the guide RNA (gRNA) comprises a CRISPR RNA (crRNA) and a hybridized trans-acting CRISPR RNA (tracrRNA), both being essential in guided DNA targeting functions. Although tracrRNAs are diverse in sequence and structure across type II CRISPR systems, the programmability of crRNA-tracrRNA hybridization for Cas9 is not fully understood. Here, we reveal the programmability of crRNA-tracrRNA hybridization for Streptococcus pyogenes Cas9, and in doing so, redefine the capabilities of Cas9 proteins and the sources of crRNAs, providing new biosensing applications for type II CRISPR systems. By reprogramming the crRNA-tracrRNA hybridized sequence, we show that engineered crRNA-tracrRNA interactions can not only enable the design of orthogonal cellular computing devices but also facilitate the hijacking of endogenous small RNAs/mRNAs as crRNAs. We subsequently describe how these re-engineered gRNA pairings can be implemented as RNA sensors, capable of monitoring the transcriptional activity of various environment-responsive genomic genes, or detecting SARS-CoV-2 RNA in vitro, as an Atypical gRNA-activated Transcription Halting Alarm (AGATHA) biosensor.