Browsing by Author "Dr. Negash Medhin, Committee Member"

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    Design of a Public Logistics Network.
    (2004-05-20) Bansal, Amogh; Dr. Michael G. Kay, Committee Chair; Dr. Russell E. King, Committee Member; Dr. Negash Medhin, Committee Member
    This thesis presents a design for a public logistics network (PLN) covering the continental United States. A systematic approach is developed to determine the initial national road network using only the Interstate highways and part of the U.S. highways. Various heuristics are developed for generating the underlying road network. In two of the heuristics, every Interstate node is used and then U.S. highway nodes are added to the road network to supplement the Interstate nodes. Another heuristic generates the network by directly joining the roads (based on shortest time routes) between the cities of more than certain population. The results from testing show that the later heuristic performs better than the former ones. This road network is then used for developing the PLN by selecting some of its nodes as the locations for distribution centers (DCs). The PLN is then developed by removing and adding arcs in a reduced network obtained by Delaunay triangulation of the selected DC nodes in the underlying road network. Given the number and location of DCs on this underlying network, the minimum average transport time for a package is used as the criterion to compare alternative PLN designs. The package demand used to determine the minimum average transport time is proportional to the population at each five-digit zip code centroid surrounding each DC. Effects of different parameters on the design of the PLN are studied. Finally, a genetic algorithm is used to get the optimal public logistics network for the entire U.S.
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    Simple Strategies to Improve Data Warehouse Performance
    (2004-08-16) Mathews, Reena; Dr. Robert B. Handfield, Committee Chair; Dr. Yahya Fathi, Committee Co-Chair; Dr. Negash Medhin, Committee Member
    Data warehouse management is fast becoming one of the most popular and important topics in industries today. For business executives, it promises significant competitive advantage for their companies, while presenting the information system managers a way to overcome the obstructions in providing business information to managers and other users. Here the company is going through the problem of inefficient performance of its data warehouse. To find an appropriate solution to this problem we first try to understand the data warehouse concept and its basic architecture, followed by an in depth study of the company data warehouse and the various issues affecting it. We propose and evaluate a set of solutions including classification of suppliers, implementing corporate commodity classification and coding system, obtaining level three spend details for PCard purchases, etc. The experimental results show considerable improvement in the data quality and the data warehouse performance. We further support these recommendations by evaluating the return on investment for improved quality data. Lastly, we discuss the future scope and other possible improvement techniques for obtaining better results.
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    Solving the Max-Cut Problem using Semidefinite Optimization in a Cutting Plane Algorithm.
    (2008-08-18) Sullivan, Eric Joseph; Dr. Hien Tran, Committee Member; Dr. Negash Medhin, Committee Member; Dr. Kartik Sivaramakrishnan, Committee Chair
    A central graph theory problem that occurs in experimental physics, circuit layout, and computational linear algebra is the max-cut problem. The max-cut problem is to find a bipartition of the vertex set of a graph with the objective to maximize the number of edges between the two partitions. The problem is NP-hard, i.e., there is no efficient algorithm to solve the max-cut problem to optimality. We propose a semidefinite programming based cutting plane algorithm to solve the max-cut problem to optimality in this thesis. Semidefinite programming (SDP) is a convex optimization problem, where the variables are symmetric matrices. An SDP has a linear objective function, linear constraints, and also convex constraints requiring the matrices to be positive semidefinite. Interior point methods can efficiently solve SDPs and several software implementations like SDPT-3 are currently available. Each iteration of our cutting plane algorithm has the following features: (a) an SDP relaxation of the max-cut problem, whose objective function provides an upper bound on the max-cut value, (b) the Goemans-Williamson heuristic to round the solution to the SDP relaxation into a feasible cut vector, that provides a lower bound on the max-cut value, and (c) a separation oracle that returns cutting planes to cut off the optimal solution to the SDP relaxation that is not in the max-cut polytope. Steps (a), (b), and (c) are repeated until the algorithm finds an optimal solution to the max-cut problem. We have implemented the above cutting plane algorithm in MATLAB. Step (a) of the program uses SDPT-3 a primal-dual interior point software for solving the SDP relaxations. Step (c) of the algorithm returns triangle inequalities specific to the max-cut problem as cutting planes. We report our computational results with the algorithm on randomly generated graphs, where the number of vertices and the density of the edges vary between 5 to 50 and 0.1 to 1.0, respectively.