Solving support vector machine classification problems and their applications to supplier selection

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Show simple item record Kim, Gitae 2011-05-05T21:09:35Z 2011-05-05T21:09:35Z 2011-05-05
dc.description.abstract Recently, interdisciplinary (management, engineering, science, and economics) collaboration research has been growing to achieve the synergy and to reinforce the weakness of each discipline. Along this trend, this research combines three topics: mathematical programming, data mining, and supply chain management. A new pegging algorithm is developed for solving the continuous nonlinear knapsack problem. An efficient solving approach is proposed for solving the ν-support vector machine for classification problem in the field of data mining. The new pegging algorithm is used to solve the subproblem of the support vector machine problem. For the supply chain management, this research proposes an efficient integrated solving approach for the supplier selection problem. The support vector machine is applied to solve the problem of selecting potential supplies in the procedure of the integrated solving approach. In the first part of this research, a new pegging algorithm solves the continuous nonlinear knapsack problem with box constraints. The problem is to minimize a convex and differentiable nonlinear function with one equality constraint and box constraints. Pegging algorithm needs to calculate primal variables to check bounds on variables at each iteration, which frequently is a time-consuming task. The newly proposed dual bound algorithm checks the bounds of Lagrange multipliers without calculating primal variables explicitly at each iteration. In addition, the calculation of the dual solution at each iteration can be reduced by a proposed new method for updating the solution. In the second part, this research proposes several streamlined solution procedures of ν-support vector machine for the classification. The main solving procedure is the matrix splitting method. The proposed method in this research is a specified matrix splitting method combined with the gradient projection method, line search technique, and the incomplete Cholesky decomposition method. The method proposed can use a variety of methods for line search and parameter updating. Moreover, large scale problems are solved with the incomplete Cholesky decomposition and some efficient implementation techniques. To apply the research findings in real-world problems, this research developed an efficient integrated approach for supplier selection problems using the support vector machine and the mixed integer programming. Supplier selection is an essential step in the procurement processes. For companies considering maximizing their profits and reducing costs, supplier selection requires seeking satisfactory suppliers and allocating proper orders to the selected suppliers. In the early stage of supplier selection, a company can use the support vector machine classification to choose potential qualified suppliers using specific criteria. However, the company may not need to purchase from all qualified suppliers. Once the company determines the amount of raw materials and components to purchase, the company then selects final suppliers from which to order optimal order quantities at the final stage of the process. Mixed integer programming model is then used to determine final suppliers and allocates optimal orders at this stage. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Support Vector Machine en_US
dc.subject Nonlinear Knapsack Problem en_US
dc.subject Supplier Selection en_US
dc.subject Convex Optimization en_US
dc.subject Supply Chain Management en_US
dc.subject Classification en_US
dc.title Solving support vector machine classification problems and their applications to supplier selection en_US
dc.type Dissertation en_US Doctor of Philosophy en_US
dc.description.level Doctoral en_US
dc.description.department Department of Industrial & Manufacturing Systems Engineering en_US
dc.description.advisor Chih-Hang Wu en_US
dc.subject.umi Engineering (0537) en_US 2011 en_US May en_US

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