Person:
Lu, Jye-Chyi

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https://hdl.handle.net/1853/71479
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    A Continuous Approximation Approach for the Integrated Facility-Inventory Allocation Problem
    (Georgia Institute of Technology, 2009) Mangotra, Divya ; Lu, Jye-Chyi ; Tsao, Yu-Chung
    In today's business many companies have a complex distribution network with several national and regional distribution centers. In this paper, we study an integrated facility location and inventory allocation problem for designing a distribution network with multiple national distribution centers (NDCs) and retailers. The key decisions are where to locate the regional distribution centers (RDCs), how to assign retail stores to RDCs and what should be the inventory policy at the different locations such that the total network cost is minimized. We model our problem using a Type-I (probability of stock-outs) service level measure. This paper presents a continuous approximation (CA) model for solving the problem described above. The model takes a nonlinear form and solution techniques are developed using the theory of nonlinear programming. The main contribution of this work lies in developing a refined CA modeling technique when the discrete data cannot be modeled by a continuous function. Our methodology is illustrated on a real life application of a leading US retailer. Numerical analysis suggests that the total network cost is significantly lower in the case of the integrated model as compared with the non-integrated model. It also shows that the regular CA approach leads to a solution which is inferior to the solution obtained by the modified CA approach. Our analysis shows that the type of service measure used affects the network design.
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    A Fill-Rate Service Level Model for Integrated Network Design and Inventory Allocation Problem
    (Georgia Institute of Technology, 2009) Mangotra, Divya ; Lu, Jye-Chyi ; Tsao, Yu-Chung
    Today’s global economy rely heavily on transportation and warehousing to deliver goods. Outsourcing has also made the distribution networks complex with several layers of national (import) distribution centers (NDCs) and regional distribution centers (RDCs) between suppliers and retailers. More distribution nodes in the supply chain also means more inventory stockpiles. We present an integrated facility location and inventory allocation problem for designing a distribution network with multiple NDCs and retailers. The key decisions are where to locate the regional distribution centers and how much inventory to hold at the different nodes of the distribution network such that the total network cost is minimized. A Type-II (fill-rate) service level measure is used in this analysis for modeling the safety stock inventory and the inventory cost analysis is based on the continuous review batch ordering policy. Our analysis shows that the type of service measure used affects the network design.
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    GOALI: processing, system modeling and process control for complicated functional data
    (Georgia Institute of Technology, 2008-06-01) Lu, Jye-Chyi ; Kvam, Paul H.
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    Wavelet-based Data Reduction Techniques for Process Fault Detection
    (Georgia Institute of Technology, 2004) Jeong, Myong-Kee ; Lu, Jye-Chyi ; Huo, Xiaoming ; Vidakovic, Brani ; Chen, Di
    To handle potentially large and complicated nonstationary data curves, this article presents new data reduction methods based on the discrete wavelet transform. The methods minimize objective functions to balance the tradeoff between data reduction and modeling accuracy. Theoretic investigations provide the optimality of the methods and the large-sample distribution of a closedform estimate of the thresholding parameter. An upper bound of errors in signal approximation (or estimation) is derived. Based on evaluation studies with popular testing curves and real-life data sets, the proposed methods demonstrate their competitiveness to the existing engineering data-compression and statistical data-denoising methods for achieving the data reduction goals. Further experimentation with a tree-based classification procedure for identifying process fault classes illustrates the potential of the data-reduction tools. Extension of the engineering scalogram to the reduced-size semiconductor fabrication data leads to a visualization tool for monitoring and understanding process problems.