Browsing by Author "Dr. Robert E. Young, Committee Member"

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Linear Program Construction Using Metamodeling
(2003-04-02) Parks, Judith-Marie Tyler; Dr. Russell E. King, Committee Member; Dr. Marc-David Cohen, Committee Member; Dr. Thom J. Hodgson, Committee Chair; Dr. Robert E. Young, Committee Member; Dr. Michael G. Kay, Committee Member
One of the most significant trends in data warehousing today is the integration of Metadata into data warehousing tools. A data warehouse is an area which exists on computer systems that is used for holding all of the data that an organization might possess. Metadata is 'data about data,' a dictionary and summary of data, that is held in a system catalog that is contained in a data warehouse. The purpose of this dissertation is four-fold: to show that by examining a database's system catalog, information can be extracted from it that can be used to develop a structure for building operations research applications. To show that a database's system catalog can be modified to hold the structure and the definition of a linear programming model. To show that a data table containing the linear programming model constraints can be automatically constructed based on the contents of the modified system catalog. And finally, to show that the modified system catalog can be used to guide a user in developing objective functions based on a given set of model constraints. Thus, the main contribution of the work is that it furthers the hybrid area of information technology/mathematical programming by exploiting metadata, as opposed to raw data, that is held in a data warehouse.
A Methodology to Evaluate Nuclear Waste Transmutation/Fuel Cycle Systems
(2007-04-24) Li, Jun; Dr. David N. McNelis, Committee Member; Dr. Paul J. Turinsky, Committee Member; Dr. Robert E. Young, Committee Member; Dr. Man-sung Yim, Committee Chair
The nuclear waste issue is a major challenge to the nuclear energy industry. To reduce the nuclear waste impact, a number of advanced nuclear fuel cycles and transmutation schemes are being investigated. Reported herein is a methodology which was developed to evaluate and compare nuclear fuel cycles based on repository performance, proliferation resistance performance and fuel cycle cost. To evaluate the repository performance efficiently, a simplified repository performance model was developed based on the Yucca Mountain Repository. By considering the temperature limits at different locations in the repository, maximum loading is estimated for given nuclear waste characteristics. The enrivonmental impact from the maximum loaded reprository is investigated in term of projected dose and health index based on accumulated risk. A fuzzy logic based barrier method was developed to assess the proliferation resistance of three different nuclear fuel cycles. This model gives quantitative proliferation resistance information from the beginning to the end of a full fuel cycle. A simple fuel cycle cost model was also used. Based on assumed nonproliferation charges, an adjusted fuel cycle cost was evaluated which included the impact from repository and proliferation resistance performance to the overall fuel cycle cost. A case study investigates the three fuel cycles: PWR-OT (Pressurized Water Reactor-Once Through), MOX (Mixed Oxide) and DUPIC (Direct Use of spent PWR fuel in CANDU). The PWR-OT cycle provides the highest level of proliferation resistance and lowest fuel cycle cost while the DUPIC cycle provides for maximum repository loading (based on the total electricity generated). The adjusted fuel cycle cost was found to be an inadequate means of combining repository impact, proliferation resistance, and cost to affect fuel cycle selection decisions. An alternative method of using an adjusted total electricity generation cost is presented.
Performance Analysis of Intelligent Supply Chain Networks
(2002-06-03) Parlikad, Ajith Kumar Parlikad Narayanan; Dr. Michael G. Kay, Committee Chair; Dr. Robert E. Young, Committee Member; Dr. Cecil C. Bozarth, Committee Member
Supply Chain Management has become the primary competitive weapon in many industries. This thesis describes a model of intelligent supply chain networks that will improve information visibility and flow within the supply chain. In the proposed model, products will have the intelligence to direct themselves throughout the distribution network and will have the capability to be purchased and sold while in transit. The report gives an overview of the supporting technologies that make such a supply chain a reality. It is intended to provide a preliminary outlook at the various issues related to implementation and could be used for future research as a basis for building the infrastructure required for the new model. An effort has been made to provide a design structure for XML identification tags, which will be one of the most critical components of the system. In addition to that, this report will describe the results of a simulation analysis using a model of a hypothetical public logistics network covering the southeastern United States. The network consists of 36 nodes representing the public distribution centers and 59 arcs, which represents various interstate highways connecting them. The simulations help identify the critical parameters associated with the material flow through the network and provide an insight into the capacity requirement of the public distribution centers. The results of the simulations will be used as a benchmark for future research and development associated with building an actual negotiating agent model.
Solving Complex Modeling of System-on-a-Chip (SOC) Test Automation and Optimal Resource Allocation by Neural Networks
(2003-01-13) Kloypayan, Jirawan; Dr. Krishnendu Chakrabarty, Committee Member; Dr. Elmor Peterson, Committee Member; Dr. Ezat T. Sanii, Committee Member; Dr. Robert E. Young, Committee Member; Dr. Yuan-Shin Lee, Committee Chair
The objective of this research is to optimize the testing time and test resource allocation for System-on-a-Chip (SOC). The mathematical formulation and the neural networks with different techniques are proposed to solve these SOC test problems. First, a fixed-weight neural network combined with heuristic algorithms has been developed to solve the SOC test scheduling problems. The objective of this SOC test automation is to minimize the SOC testing time subject to different constraints: (i) precedence constraint, (ii) resource constraint, (iii) core constraint, and (iv) power constraint. Heuristic algorithms are often used to prevent the neural network from getting trapped in a local optima. The developed neural network can effectively solve the SOC test scheduling models with disjunctive constraints. The results show that the proposed method can efficiently solve a large-size SOC test scheduling problem within reasonable computing time. Second, to solve the resource allocation and the width selection problems for SOC test automation, a maximum neural network (MNN) has been proposed in this research for handling more complex SOC test problems. The SOC test automation problem with resource allocation is a NP-hard problem. The proposed maximum neural network can be used to solve the NP-hard SOC test problems within polynomial time. The results show that, by using the developed maximum neural network, the overall testing time for the SOC can be minimized with optimal resource allocation and test access mechanism (TAM) width selection. The computation time of the proposed method is significantly less than the time for traditional methods such as the integer linear programming (ILP) or heuristic algorithms. Third, the SOC test automation problems with core test wrapper design have been studied in this research. The core test wrapper design provides an interface between the core and the SOC in which the core is embedded. After the core test wrapper is designed, the total SOC testing time and the resource allocation for SOC test automation are optimized by using the developed maximum neural network. The proposed method is tested on five SOC benchmarks. The results show that it is possible to find the optimal SOC testing time of the complex SOC systems with shorter computation time than with the existing traditional methods. The techniques presented in this research can be used in test automation for System-on-a-Chip (SOC) design.