Person:
Xie, Yao

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Publication Search Results

Now showing 1 - 6 of 6
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    Demand-supply alignment in supply chain networks with access to hyperconnected production options
    (Georgia Institute of Technology, 2023-06) Pothen, Ashwin ; Inan, Mahmut Metin ; Montreuil, Benoit ; Lauras, Matthieu ; Benaben, Frederick ; Xie, Yao
    Supply chain networks today comprise of various decentralized actors, subject to constantly evolving challenges and customer expectations, and operate in a volatile, uncertain, and disruption-prone environment. These challenges and complexities bring in informational and material flow distortions, making it hard to align demand and supply with agility. Building a centralized optimization model for such complex systems tends to be computationally expensive and unscalable for real-world application. With this motivation, we propose a novel, real-world applicable multi-agent-based approach for collaborative and agile demand-supply alignment, through dynamic prediction-driven planning and operational decision-making. We first demonstrate the applicability and configurability of our approach with a real-world supply chain network operating in a stochastic and disruptive environment, with the desired characteristics in congruence with the Physical Internet framework. We then demonstrate the simulation-testing capability of our approach by highlighting the potential benefits of leveraging a hyperconnected network of open certified production options.
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    Online Detection of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes
    (Georgia Institute of Technology, 2023-06) Yamin, Khurram ; Wang, Haoyun ; Montreuil, Benoit ; Xie, Yao
    In this Paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company. To accomplish this, we utilize a modified CUSUM (Cumulative Sum) procedure on the company's spatial-temporal order data as well as a GLR (Generalized Likelihood Ratio) based method. We model the order data using the Hawkes Process Network, a multi-dimensional self and mutually exciting point process, by discretizing the spatial data and treating each order as an event that has a corresponding node and time. We apply the methodologies on the company's most ordered item on a national scale and perform a deep dive into a single state. Because the item was ordered infrequently in the state compared to the nation, this approach allows us to show efficacy upon different degrees of data sparsity. Furthermore, it showcases use potential across differing levels of spatial detail.
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    Spatio-temporal Arrival Prediction over Hyperconnected Logistics Networks
    (Georgia Institute of Technology, 2021-06) Xu, Yujia ; Xie, Yao ; Montreuil, Benoit
    Arrival prediction is a vital component in supply chain and logistics. Planning and operational decisions depend on predictions. Hyperconnected logistics enable a new opportunity for prediction by capturing interaction and correlation between different locations and over time in the network. Arrivals at one location may have a non-homogeneous influence on future arrivals at other nearby locations. To capture the temporal dependence of past events, we aim to Introduce a simple arrival distribution prediction approach; Propose a novel method to model and predict arrival events from spatio temporal sequential data based on a spatio-temporal interactive Bernoulli process, which can capture the spatio-temporal correlations and interactions without assuming time-decaying influence; Make arrival predictions for any locations at any future time.
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    ML@GT Lab presents LAB LIGHTNING TALKS 2020
    ( 2020-12-04) AlRegib, Ghassan ; Chau, Duen Horng ; Chava, Sudheer ; Cohen, Morris B. ; Davenport, Mark A. ; Desai, Deven ; Dovrolis, Constantine ; Essa, Irfan ; Gupta, Swati ; Huo, Xiaoming ; Kira, Zsolt ; Li, Jing ; Maguluri, Siva Theja ; Pananjady, Ashwin ; Prakash, B. Aditya ; Riedl, Mark O. ; Romberg, Justin ; Xie, Yao ; Zhang, Xiuwei
    Labs affiliated with the Machine Learning Center at Georgia Tech (ML@GT) will have the opportunity to share their research interests, work, and unique aspects of their lab in three minutes or less to interested graduate students, Georgia Tech faculty, and members of the public. Participating labs include: Yao’s Group - Yao Xie, H. Milton Stewart School of Industrial Systems and Engineering (ISyE); Huo Lab - Xiaoming Huo, ISyE; LF Radio Lab – Morris Cohen, School of Electrical Computing and Engineering (ECE); Polo Club of Data Science – Polo Chau, CSE; Network Science – Constantine Dovrolis, School of Computer Science; CLAWS – Srijan Kumar, CSE; Control, Optimization, Algorithms, and Randomness (COAR) Lab – Siva Theja Maguluri, ISyE; Entertainment Intelligence Lab and Human Centered AI Lab – Mark Riedl, IC; Social and Language Technologies (SALT) Lab – Diyi Yang, IC; FATHOM Research Group – Swati Gupta, ISyE; Zhang's CompBio Lab – Xiuwei Zhang, CSE; Statistical Machine Learning - Ashwin Pananjady, ISyE and ECE; AdityaLab - B. Aditya Prakash, CSE; OLIVES - Ghassan AlRegib, ECE; Robotics Perception and Learning (RIPL) – Zsolt Kira, IC; Eye-Team - Irfan Essa, IC; and Mark Davenport, ECE.
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    Health Systems: The Next Generation 2019 - Rapid Fire Research Presentations
    ( 2019-11-12) Botchwey, Nisha ; Mei, Yajun ; Nazzal, Dima ; West, Leanne ; Xie, Yao
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    Machine Learning and Anomaly Detection
    (Georgia Institute of Technology, 2016-09-06) Xie, Yao